diff --git a/go.mod b/go.mod index c5fc246..242dfde 100644 --- a/go.mod +++ b/go.mod @@ -1,12 +1,19 @@ module github.com/miguelmota/go-ethereum-hdwallet -go 1.12 +go 1.18 require ( - github.com/allegro/bigcache v1.2.1 // indirect - github.com/btcsuite/btcd v0.21.0-beta + github.com/btcsuite/btcd v0.22.1 github.com/btcsuite/btcutil v1.0.3-0.20201208143702-a53e38424cce github.com/davecgh/go-spew v1.1.1 - github.com/ethereum/go-ethereum v1.10.4 - github.com/tyler-smith/go-bip39 v1.0.1-0.20181017060643-dbb3b84ba2ef + github.com/ethereum/go-ethereum v1.10.17 + github.com/tyler-smith/go-bip39 v1.1.0 +) + +require ( + github.com/btcsuite/btcd/btcec/v2 v2.2.0 // indirect + github.com/btcsuite/btcd/chaincfg/chainhash v1.0.1 // indirect + github.com/decred/dcrd/dcrec/secp256k1/v4 v4.0.1 // indirect + golang.org/x/crypto v0.0.0-20220518034528-6f7dac969898 // indirect + golang.org/x/sys v0.0.0-20220520151302-bc2c85ada10a // indirect ) diff --git a/go.sum b/go.sum index 5bb27a0..58b8b39 100644 --- a/go.sum +++ b/go.sum @@ -18,19 +18,9 @@ cloud.google.com/go/storage v1.0.0/go.mod h1:IhtSnM/ZTZV8YYJWCY8RULGVqBDmpoyjwiy cloud.google.com/go/storage v1.5.0/go.mod h1:tpKbwo567HUNpVclU5sGELwQWBDZ8gh0ZeosJ0Rtdos= collectd.org v0.3.0/go.mod h1:A/8DzQBkF6abtvrT2j/AU/4tiBgJWYyh0y/oB/4MlWE= dmitri.shuralyov.com/gpu/mtl v0.0.0-20190408044501-666a987793e9/go.mod h1:H6x//7gZCb22OMCxBHrMx7a5I7Hp++hsVxbQ4BYO7hU= -github.com/Azure/azure-pipeline-go v0.2.1/go.mod h1:UGSo8XybXnIGZ3epmeBw7Jdz+HiUVpqIlpz/HKHylF4= -github.com/Azure/azure-pipeline-go v0.2.2/go.mod h1:4rQ/NZncSvGqNkkOsNpOU1tgoNuIlp9AfUH5G1tvCHc= -github.com/Azure/azure-storage-blob-go v0.7.0/go.mod h1:f9YQKtsG1nMisotuTPpO0tjNuEjKRYAcJU8/ydDI++4= -github.com/Azure/go-autorest/autorest v0.9.0/go.mod h1:xyHB1BMZT0cuDHU7I0+g046+BFDTQ8rEZB0s4Yfa6bI= -github.com/Azure/go-autorest/autorest/adal v0.5.0/go.mod h1:8Z9fGy2MpX0PvDjB1pEgQTmVqjGhiHBW7RJJEciWzS0= -github.com/Azure/go-autorest/autorest/adal v0.8.0/go.mod h1:Z6vX6WXXuyieHAXwMj0S6HY6e6wcHn37qQMBQlvY3lc= -github.com/Azure/go-autorest/autorest/date v0.1.0/go.mod h1:plvfp3oPSKwf2DNjlBjWF/7vwR+cUD/ELuzDCXwHUVA= -github.com/Azure/go-autorest/autorest/date v0.2.0/go.mod h1:vcORJHLJEh643/Ioh9+vPmf1Ij9AEBM5FuBIXLmIy0g= -github.com/Azure/go-autorest/autorest/mocks v0.1.0/go.mod h1:OTyCOPRA2IgIlWxVYxBee2F5Gr4kF2zd2J5cFRaIDN0= -github.com/Azure/go-autorest/autorest/mocks v0.2.0/go.mod h1:OTyCOPRA2IgIlWxVYxBee2F5Gr4kF2zd2J5cFRaIDN0= -github.com/Azure/go-autorest/autorest/mocks v0.3.0/go.mod h1:a8FDP3DYzQ4RYfVAxAN3SVSiiO77gL2j2ronKKP0syM= -github.com/Azure/go-autorest/logger v0.1.0/go.mod h1:oExouG+K6PryycPJfVSxi/koC6LSNgds39diKLz7Vrc= -github.com/Azure/go-autorest/tracing v0.5.0/go.mod h1:r/s2XiOKccPW3HrqB+W0TQzfbtp2fGCgRFtBroKn4Dk= +github.com/Azure/azure-sdk-for-go/sdk/azcore v0.21.1/go.mod h1:fBF9PQNqB8scdgpZ3ufzaLntG0AG7C1WjPMsiFOmfHM= +github.com/Azure/azure-sdk-for-go/sdk/internal v0.8.3/go.mod h1:KLF4gFr6DcKFZwSuH8w8yEK6DpFl3LP5rhdvAb7Yz5I= +github.com/Azure/azure-sdk-for-go/sdk/storage/azblob v0.3.0/go.mod h1:tPaiy8S5bQ+S5sOiDlINkp7+Ef339+Nz5L5XO+cnOHo= github.com/BurntSushi/toml v0.3.1/go.mod h1:xHWCNGjB5oqiDr8zfno3MHue2Ht5sIBksp03qcyfWMU= github.com/BurntSushi/xgb v0.0.0-20160522181843-27f122750802/go.mod h1:IVnqGOEym/WlBOVXweHU+Q+/VP0lqqI8lqeDx9IjBqo= github.com/DATA-DOG/go-sqlmock v1.3.3/go.mod h1:f/Ixk793poVmq4qj/V1dPUg2JEAKC73Q5eFN3EC/SaM= @@ -44,8 +34,6 @@ github.com/ajstarks/svgo v0.0.0-20180226025133-644b8db467af/go.mod h1:K08gAheRH3 github.com/alecthomas/template v0.0.0-20160405071501-a0175ee3bccc/go.mod h1:LOuyumcjzFXgccqObfd/Ljyb9UuFJ6TxHnclSeseNhc= github.com/alecthomas/units v0.0.0-20151022065526-2efee857e7cf/go.mod h1:ybxpYRFXyAe+OPACYpWeL0wqObRcbAqCMya13uyzqw0= github.com/allegro/bigcache v1.2.1-0.20190218064605-e24eb225f156/go.mod h1:Cb/ax3seSYIx7SuZdm2G2xzfwmv3TPSk2ucNfQESPXM= -github.com/allegro/bigcache v1.2.1 h1:hg1sY1raCwic3Vnsvje6TT7/pnZba83LeFck5NrFKSc= -github.com/allegro/bigcache v1.2.1/go.mod h1:Cb/ax3seSYIx7SuZdm2G2xzfwmv3TPSk2ucNfQESPXM= github.com/andreyvit/diff v0.0.0-20170406064948-c7f18ee00883/go.mod h1:rCTlJbsFo29Kk6CurOXKm700vrz8f0KW0JNfpkRJY/8= github.com/apache/arrow/go/arrow v0.0.0-20191024131854-af6fa24be0db/go.mod h1:VTxUBvSJ3s3eHAg65PNgrsn5BtqCRPdmyXh6rAfdxN0= github.com/aws/aws-sdk-go-v2 v1.2.0/go.mod h1:zEQs02YRBw1DjK0PoJv3ygDYOFTre1ejlJWl8FwAuQo= @@ -62,18 +50,21 @@ github.com/beorn7/perks v1.0.0/go.mod h1:KWe93zE9D1o94FZ5RNwFwVgaQK1VOXiVxmqh+Ce github.com/bmizerany/pat v0.0.0-20170815010413-6226ea591a40/go.mod h1:8rLXio+WjiTceGBHIoTvn60HIbs7Hm7bcHjyrSqYB9c= github.com/boltdb/bolt v1.3.1/go.mod h1:clJnj/oiGkjum5o1McbSZDSLxVThjynRyGBgiAx27Ps= github.com/btcsuite/btcd v0.20.1-beta/go.mod h1:wVuoA8VJLEcwgqHBwHmzLRazpKxTv13Px/pDuV7OomQ= -github.com/btcsuite/btcd v0.21.0-beta h1:At9hIZdJW0s9E/fAz28nrz6AmcNlSVucCH796ZteX1M= -github.com/btcsuite/btcd v0.21.0-beta/go.mod h1:ZSWyehm27aAuS9bvkATT+Xte3hjHZ+MRgMY/8NJ7K94= +github.com/btcsuite/btcd v0.22.1 h1:CnwP9LM/M9xuRrGSCGeMVs9iv09uMqwsVX7EeIpgV2c= +github.com/btcsuite/btcd v0.22.1/go.mod h1:wqgTSL29+50LRkmOVknEdmt8ZojIzhuWvgu/iptuN7Y= +github.com/btcsuite/btcd/btcec/v2 v2.1.2/go.mod h1:ctjw4H1kknNJmRN4iP1R7bTQ+v3GJkZBd6mui8ZsAZE= +github.com/btcsuite/btcd/btcec/v2 v2.2.0 h1:fzn1qaOt32TuLjFlkzYSsBC35Q3KUjT1SwPxiMSCF5k= +github.com/btcsuite/btcd/btcec/v2 v2.2.0/go.mod h1:U7MHm051Al6XmscBQ0BoNydpOTsFAn707034b5nY8zU= +github.com/btcsuite/btcd/chaincfg/chainhash v1.0.0/go.mod h1:7SFka0XMvUgj3hfZtydOrQY2mwhPclbT2snogU7SQQc= +github.com/btcsuite/btcd/chaincfg/chainhash v1.0.1 h1:q0rUy8C/TYNBQS1+CGKw68tLOFYSNEs0TFnxxnS9+4U= +github.com/btcsuite/btcd/chaincfg/chainhash v1.0.1/go.mod h1:7SFka0XMvUgj3hfZtydOrQY2mwhPclbT2snogU7SQQc= github.com/btcsuite/btclog v0.0.0-20170628155309-84c8d2346e9f/go.mod h1:TdznJufoqS23FtqVCzL0ZqgP5MqXbb4fg/WgDys70nA= github.com/btcsuite/btcutil v0.0.0-20190425235716-9e5f4b9a998d/go.mod h1:+5NJ2+qvTyV9exUAL/rxXi3DcLg2Ts+ymUAY5y4NvMg= -github.com/btcsuite/btcutil v1.0.2/go.mod h1:j9HUFwoQRsZL3V4n+qG+CUnEGHOarIxfC3Le2Yhbcts= github.com/btcsuite/btcutil v1.0.3-0.20201208143702-a53e38424cce h1:YtWJF7RHm2pYCvA5t0RPmAaLUhREsKuKd+SLhxFbFeQ= github.com/btcsuite/btcutil v1.0.3-0.20201208143702-a53e38424cce/go.mod h1:0DVlHczLPewLcPGEIeUEzfOJhqGPQ0mJJRDBtD307+o= github.com/btcsuite/go-socks v0.0.0-20170105172521-4720035b7bfd/go.mod h1:HHNXQzUsZCxOoE+CPiyCTO6x34Zs86zZUiwtpXoGdtg= github.com/btcsuite/goleveldb v0.0.0-20160330041536-7834afc9e8cd/go.mod h1:F+uVaaLLH7j4eDXPRvw78tMflu7Ie2bzYOH4Y8rRKBY= -github.com/btcsuite/goleveldb v1.0.0/go.mod h1:QiK9vBlgftBg6rWQIj6wFzbPfRjiykIEhBH4obrXJ/I= github.com/btcsuite/snappy-go v0.0.0-20151229074030-0bdef8d06723/go.mod h1:8woku9dyThutzjeg+3xrA5iCpBRH8XEEg3lh6TiUghc= -github.com/btcsuite/snappy-go v1.0.0/go.mod h1:8woku9dyThutzjeg+3xrA5iCpBRH8XEEg3lh6TiUghc= github.com/btcsuite/websocket v0.0.0-20150119174127-31079b680792/go.mod h1:ghJtEyQwv5/p4Mg4C0fgbePVuGr935/5ddU9Z3TmDRY= github.com/btcsuite/winsvc v1.0.0/go.mod h1:jsenWakMcC0zFBFurPLEAyrnc/teJEM1O46fmI40EZs= github.com/c-bata/go-prompt v0.2.2/go.mod h1:VzqtzE2ksDBcdln8G7mk2RX9QyGjH+OVqOCSiVIqS34= @@ -91,50 +82,64 @@ github.com/cloudflare/cloudflare-go v0.14.0/go.mod h1:EnwdgGMaFOruiPZRFSgn+TsQ3h github.com/consensys/bavard v0.1.8-0.20210406032232-f3452dc9b572/go.mod h1:Bpd0/3mZuaj6Sj+PqrmIquiOKy397AKGThQPaGzNXAQ= github.com/consensys/gnark-crypto v0.4.1-0.20210426202927-39ac3d4b3f1f/go.mod h1:815PAHg3wvysy0SyIqanF8gZ0Y1wjk/hrDHD/iT88+Q= github.com/cpuguy83/go-md2man/v2 v2.0.0-20190314233015-f79a8a8ca69d/go.mod h1:maD7wRr/U5Z6m/iR4s+kqSMx2CaBsrgA7czyZG/E6dU= +github.com/creack/pty v1.1.9/go.mod h1:oKZEueFk5CKHvIhNR5MUki03XCEU+Q6VDXinZuGJ33E= +github.com/cyberdelia/templates v0.0.0-20141128023046-ca7fffd4298c/go.mod h1:GyV+0YP4qX0UQ7r2MoYZ+AvYDp12OF5yg4q8rGnyNh4= github.com/dave/jennifer v1.2.0/go.mod h1:fIb+770HOpJ2fmN9EPPKOqm1vMGhB+TwXKMZhrIygKg= github.com/davecgh/go-spew v0.0.0-20171005155431-ecdeabc65495/go.mod 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v0.0.0-20180321215751-8460e604b9de/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA= golang.org/x/exp v0.0.0-20180807140117-3d87b88a115f/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA= golang.org/x/exp v0.0.0-20190121172915-509febef88a4/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA= @@ -379,11 +396,9 @@ golang.org/x/mod v0.1.0/go.mod h1:0QHyrYULN0/3qlju5TqG8bIK38QM8yzMo5ekMj3DlcY= golang.org/x/mod v0.1.1-0.20191105210325-c90efee705ee/go.mod h1:QqPTAvyqsEbceGzBzNggFXnrqF1CaUcvgkdR5Ot7KZg= golang.org/x/mod v0.3.0/go.mod h1:s0Qsj1ACt9ePp/hMypM3fl4fZqREWJwdYDEqhRiZZUA= golang.org/x/mod v0.4.2/go.mod h1:s0Qsj1ACt9ePp/hMypM3fl4fZqREWJwdYDEqhRiZZUA= -golang.org/x/net v0.0.0-20180719180050-a680a1efc54d/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4= golang.org/x/net v0.0.0-20180724234803-3673e40ba225/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4= golang.org/x/net v0.0.0-20180826012351-8a410e7b638d/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4= golang.org/x/net v0.0.0-20180906233101-161cd47e91fd/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4= -golang.org/x/net v0.0.0-20181011144130-49bb7cea24b1/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4= golang.org/x/net v0.0.0-20181114220301-adae6a3d119a/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4= golang.org/x/net v0.0.0-20190108225652-1e06a53dbb7e/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4= golang.org/x/net v0.0.0-20190213061140-3a22650c66bd/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4= @@ -398,10 +413,14 @@ golang.org/x/net v0.0.0-20190724013045-ca1201d0de80/go.mod h1:z5CRVTTTmAJ677TzLL golang.org/x/net v0.0.0-20191209160850-c0dbc17a3553/go.mod h1:z5CRVTTTmAJ677TzLLGU+0bjPO0LkuOLi4/5GtJWs/s= golang.org/x/net v0.0.0-20200520004742-59133d7f0dd7/go.mod h1:qpuaurCH72eLCgpAm/N6yyVIVM9cpaDIP3A8BGJEC5A= golang.org/x/net v0.0.0-20200813134508-3edf25e44fcc/go.mod h1:/O7V0waA8r7cgGh81Ro3o1hOxt32SMVPicZroKQ2sZA= +golang.org/x/net v0.0.0-20200822124328-c89045814202/go.mod h1:/O7V0waA8r7cgGh81Ro3o1hOxt32SMVPicZroKQ2sZA= +golang.org/x/net v0.0.0-20201010224723-4f7140c49acb/go.mod h1:sp8m0HH+o8qH0wwXwYZr8TS3Oi6o0r6Gce1SSxlDquU= golang.org/x/net v0.0.0-20201021035429-f5854403a974/go.mod h1:sp8m0HH+o8qH0wwXwYZr8TS3Oi6o0r6Gce1SSxlDquU= +golang.org/x/net v0.0.0-20210119194325-5f4716e94777/go.mod h1:m0MpNAwzfU5UDzcl9v0D8zg8gWTRqZa9RBIspLL5mdg= golang.org/x/net v0.0.0-20210220033124-5f55cee0dc0d/go.mod h1:m0MpNAwzfU5UDzcl9v0D8zg8gWTRqZa9RBIspLL5mdg= -golang.org/x/net v0.0.0-20210226172049-e18ecbb05110 h1:qWPm9rbaAMKs8Bq/9LRpbMqxWRVUAQwMI9fVrssnTfw= golang.org/x/net v0.0.0-20210226172049-e18ecbb05110/go.mod h1:m0MpNAwzfU5UDzcl9v0D8zg8gWTRqZa9RBIspLL5mdg= +golang.org/x/net v0.0.0-20210610132358-84b48f89b13b/go.mod h1:9nx3DQGgdP8bBQD5qxJ1jj9UTztislL4KSBs9R2vV5Y= +golang.org/x/net v0.0.0-20210805182204-aaa1db679c0d/go.mod h1:9nx3DQGgdP8bBQD5qxJ1jj9UTztislL4KSBs9R2vV5Y= golang.org/x/oauth2 v0.0.0-20180821212333-d2e6202438be/go.mod h1:N/0e6XlmueqKjAGxoOufVs8QHGRruUQn6yWY3a++T0U= golang.org/x/oauth2 v0.0.0-20190226205417-e64efc72b421/go.mod h1:gOpvHmFTYa4IltrdGE7lF6nIHvwfUNPOp7c8zoXwtLw= golang.org/x/oauth2 v0.0.0-20190604053449-0f29369cfe45/go.mod h1:gOpvHmFTYa4IltrdGE7lF6nIHvwfUNPOp7c8zoXwtLw= @@ -413,7 +432,6 @@ golang.org/x/sync v0.0.0-20181221193216-37e7f081c4d4/go.mod h1:RxMgew5VJxzue5/jJ golang.org/x/sync v0.0.0-20190227155943-e225da77a7e6/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM= golang.org/x/sync v0.0.0-20190423024810-112230192c58/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM= golang.org/x/sync v0.0.0-20190911185100-cd5d95a43a6e/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM= -golang.org/x/sync v0.0.0-20200317015054-43a5402ce75a/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM= golang.org/x/sync v0.0.0-20201020160332-67f06af15bc9/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM= golang.org/x/sync v0.0.0-20210220032951-036812b2e83c/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM= golang.org/x/sys v0.0.0-20180830151530-49385e6e1522/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY= @@ -422,6 +440,7 @@ golang.org/x/sys v0.0.0-20180909124046-d0be0721c37e/go.mod h1:STP8DvDyc/dI5b8T5h golang.org/x/sys v0.0.0-20181107165924-66b7b1311ac8/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY= golang.org/x/sys v0.0.0-20181116152217-5ac8a444bdc5/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY= golang.org/x/sys v0.0.0-20190215142949-d0b11bdaac8a/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY= +golang.org/x/sys v0.0.0-20190222072716-a9d3bda3a223/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY= golang.org/x/sys v0.0.0-20190312061237-fead79001313/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs= golang.org/x/sys v0.0.0-20190412213103-97732733099d/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs= golang.org/x/sys v0.0.0-20190502145724-3ef323f4f1fd/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs= @@ -429,32 +448,45 @@ golang.org/x/sys v0.0.0-20190507160741-ecd444e8653b/go.mod h1:h1NjWce9XRLGQEsW7w golang.org/x/sys v0.0.0-20190606165138-5da285871e9c/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs= golang.org/x/sys v0.0.0-20190624142023-c5567b49c5d0/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs= golang.org/x/sys v0.0.0-20190726091711-fc99dfbffb4e/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs= +golang.org/x/sys v0.0.0-20190813064441-fde4db37ae7a/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs= golang.org/x/sys v0.0.0-20190904154756-749cb33beabd/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs= golang.org/x/sys v0.0.0-20191005200804-aed5e4c7ecf9/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs= +golang.org/x/sys v0.0.0-20191026070338-33540a1f6037/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs= golang.org/x/sys v0.0.0-20191120155948-bd437916bb0e/go.mod 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h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ= golang.org/x/tools v0.0.0-20181030221726-6c7e314b6563/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ= @@ -485,7 +517,6 @@ golang.org/x/tools v0.1.0/go.mod h1:xkSsbof2nBLbhDlRMhhhyNLN/zl3eTqcnHD5viDpcZ0= golang.org/x/xerrors v0.0.0-20190717185122-a985d3407aa7/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0= golang.org/x/xerrors v0.0.0-20191011141410-1b5146add898/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0= golang.org/x/xerrors v0.0.0-20191204190536-9bdfabe68543/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0= -golang.org/x/xerrors v0.0.0-20200804184101-5ec99f83aff1 h1:go1bK/D/BFZV2I8cIQd1NKEZ+0owSTG1fDTci4IqFcE= golang.org/x/xerrors v0.0.0-20200804184101-5ec99f83aff1/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0= gonum.org/v1/gonum v0.0.0-20180816165407-929014505bf4/go.mod h1:Y+Yx5eoAFn32cQvJDxZx5Dpnq+c3wtXuadVZAcxbbBo= gonum.org/v1/gonum v0.0.0-20181121035319-3f7ecaa7e8ca/go.mod h1:Y+Yx5eoAFn32cQvJDxZx5Dpnq+c3wtXuadVZAcxbbBo= @@ -529,17 +560,15 @@ google.golang.org/protobuf v0.0.0-20200221191635-4d8936d0db64/go.mod h1:kwYJMbMJ google.golang.org/protobuf v0.0.0-20200228230310-ab0ca4ff8a60/go.mod h1:cfTl7dwQJ+fmap5saPgwCLgHXTUD7jkjRqWcaiX5VyM= google.golang.org/protobuf v1.20.1-0.20200309200217-e05f789c0967/go.mod h1:A+miEFZTKqfCUM6K7xSMQL9OKL/b6hQv+e19PK+JZNE= google.golang.org/protobuf v1.21.0/go.mod h1:47Nbq4nVaFHyn7ilMalzfO3qCViNmqZ2kzikPIcrTAo= -google.golang.org/protobuf v1.23.0 h1:4MY060fB1DLGMB/7MBTLnwQUY6+F09GEiz6SsrNqyzM= google.golang.org/protobuf v1.23.0/go.mod h1:EGpADcykh3NcUnDUJcl1+ZksZNG86OlYog2l/sGQquU= gopkg.in/alecthomas/kingpin.v2 v2.2.6/go.mod h1:FMv+mEhP44yOT+4EoQTLFTRgOQ1FBLkstjWtayDeSgw= gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0= -gopkg.in/check.v1 v1.0.0-20180628173108-788fd7840127 h1:qIbj1fsPNlZgppZ+VLlY7N33q108Sa+fhmuc+sWQYwY= gopkg.in/check.v1 v1.0.0-20180628173108-788fd7840127/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0= +gopkg.in/check.v1 v1.0.0-20201130134442-10cb98267c6c/go.mod h1:JHkPIbrfpd72SG/EVd6muEfDQjcINNoR0C8j2r3qZ4Q= gopkg.in/errgo.v2 v2.1.0/go.mod h1:hNsd1EY+bozCKY1Ytp96fpM3vjJbqLJn88ws8XvfDNI= gopkg.in/fsnotify.v1 v1.4.7/go.mod h1:Tz8NjZHkW78fSQdbUxIjBTcgA1z1m8ZHf0WmKUhAMys= gopkg.in/natefinch/npipe.v2 v2.0.0-20160621034901-c1b8fa8bdcce/go.mod h1:5AcXVHNjg+BDxry382+8OKon8SEWiKktQR07RKPsv1c= gopkg.in/olebedev/go-duktape.v3 v3.0.0-20200619000410-60c24ae608a6/go.mod h1:uAJfkITjFhyEEuUfm7bsmCZRbW5WRq8s9EY8HZ6hCns= -gopkg.in/tomb.v1 v1.0.0-20141024135613-dd632973f1e7 h1:uRGJdciOHaEIrze2W8Q3AKkepLTh2hOroT7a+7czfdQ= gopkg.in/tomb.v1 v1.0.0-20141024135613-dd632973f1e7/go.mod h1:dt/ZhP58zS4L8KSrWDmTeBkI65Dw0HsyUHuEVlX15mw= gopkg.in/urfave/cli.v1 v1.20.0/go.mod h1:vuBzUtMdQeixQj8LVd+/98pzhxNGQoyuPBlsXHOQNO0= gopkg.in/yaml.v2 v2.2.1/go.mod h1:hI93XBmqTisBFMUTm0b8Fm+jr3Dg1NNxqwp+5A1VGuI= @@ -547,10 +576,10 @@ gopkg.in/yaml.v2 v2.2.2/go.mod h1:hI93XBmqTisBFMUTm0b8Fm+jr3Dg1NNxqwp+5A1VGuI= gopkg.in/yaml.v2 v2.2.3/go.mod h1:hI93XBmqTisBFMUTm0b8Fm+jr3Dg1NNxqwp+5A1VGuI= gopkg.in/yaml.v2 v2.2.4/go.mod h1:hI93XBmqTisBFMUTm0b8Fm+jr3Dg1NNxqwp+5A1VGuI= gopkg.in/yaml.v2 v2.2.8/go.mod h1:hI93XBmqTisBFMUTm0b8Fm+jr3Dg1NNxqwp+5A1VGuI= -gopkg.in/yaml.v2 v2.3.0 h1:clyUAQHOM3G0M3f5vQj7LuJrETvjVot3Z5el9nffUtU= gopkg.in/yaml.v2 v2.3.0/go.mod h1:hI93XBmqTisBFMUTm0b8Fm+jr3Dg1NNxqwp+5A1VGuI= -gopkg.in/yaml.v3 v3.0.0-20200313102051-9f266ea9e77c h1:dUUwHk2QECo/6vqA44rthZ8ie2QXMNeKRTHCNY2nXvo= +gopkg.in/yaml.v2 v2.4.0/go.mod h1:RDklbk79AGWmwhnvt/jBztapEOGDOx6ZbXqjP6csGnQ= gopkg.in/yaml.v3 v3.0.0-20200313102051-9f266ea9e77c/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM= +gopkg.in/yaml.v3 v3.0.0-20210107192922-496545a6307b/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM= gotest.tools v2.2.0+incompatible/go.mod h1:DsYFclhRJ6vuDpmuTbkuFWG+y2sxOXAzmJt81HFBacw= honnef.co/go/tools v0.0.0-20190102054323-c2f93a96b099/go.mod h1:rf3lG4BRIbNafJWhAfAdb/ePZxsR/4RtNHQocxwk9r4= honnef.co/go/tools v0.0.0-20190106161140-3f1c8253044a/go.mod h1:rf3lG4BRIbNafJWhAfAdb/ePZxsR/4RtNHQocxwk9r4= diff --git a/hdwallet.go b/hdwallet.go index e2c0423..e0177e3 100644 --- a/hdwallet.go +++ b/hdwallet.go @@ -11,7 +11,7 @@ import ( "github.com/btcsuite/btcd/chaincfg" "github.com/btcsuite/btcutil/hdkeychain" - ethereum "github.com/ethereum/go-ethereum" + "github.com/ethereum/go-ethereum" "github.com/ethereum/go-ethereum/accounts" "github.com/ethereum/go-ethereum/common" "github.com/ethereum/go-ethereum/common/hexutil" @@ -280,8 +280,9 @@ func (w *Wallet) SignTx(account accounts.Account, tx *types.Transaction, chainID return nil, err } - signer := types.HomesteadSigner{} - // Sign the transaction and verify the sender to avoid hardware fault surprises + signer := types.LatestSignerForChainID(chainID) + + // Sign the transaction and verify the sender to avoid hardware fault surprises signedTx, err := types.SignTx(tx, signer, privateKey) if err != nil { return nil, err diff --git a/vendor/github.com/btcsuite/btcd/btcec/README.md b/vendor/github.com/btcsuite/btcd/btcec/README.md index 130bd20..a6dd2cf 100644 --- a/vendor/github.com/btcsuite/btcd/btcec/README.md +++ b/vendor/github.com/btcsuite/btcd/btcec/README.md @@ -1,9 +1,9 @@ btcec ===== -[![Build Status](https://travis-ci.org/btcsuite/btcd.png?branch=master)](https://travis-ci.org/btcsuite/btcec) +[![Build Status](https://github.com/btcsuite/btcd/workflows/Build%20and%20Test/badge.svg)](https://github.com/btcsuite/btcd/actions) [![ISC License](http://img.shields.io/badge/license-ISC-blue.svg)](http://copyfree.org) -[![GoDoc](https://godoc.org/github.com/btcsuite/btcd/btcec?status.png)](http://godoc.org/github.com/btcsuite/btcd/btcec) +[![GoDoc](https://pkg.go.dev/github.com/btcsuite/btcd/btcec?status.png)](https://pkg.go.dev/github.com/btcsuite/btcd/btcec) Package btcec implements elliptic curve cryptography needed for working with Bitcoin (secp256k1 only for now). It is designed so that it may be used with the @@ -25,19 +25,19 @@ $ go get -u github.com/btcsuite/btcd/btcec ## Examples -* [Sign Message](http://godoc.org/github.com/btcsuite/btcd/btcec#example-package--SignMessage) +* [Sign Message](https://pkg.go.dev/github.com/btcsuite/btcd/btcec#example-package--SignMessage) Demonstrates signing a message with a secp256k1 private key that is first parsed form raw bytes and serializing the generated signature. -* [Verify Signature](http://godoc.org/github.com/btcsuite/btcd/btcec#example-package--VerifySignature) +* [Verify Signature](https://pkg.go.dev/github.com/btcsuite/btcd/btcec#example-package--VerifySignature) Demonstrates verifying a secp256k1 signature against a public key that is first parsed from raw bytes. The signature is also parsed from raw bytes. -* [Encryption](http://godoc.org/github.com/btcsuite/btcd/btcec#example-package--EncryptMessage) +* [Encryption](https://pkg.go.dev/github.com/btcsuite/btcd/btcec#example-package--EncryptMessage) Demonstrates encrypting a message for a public key that is first parsed from raw bytes, then decrypting it using the corresponding private key. -* [Decryption](http://godoc.org/github.com/btcsuite/btcd/btcec#example-package--DecryptMessage) +* [Decryption](https://pkg.go.dev/github.com/btcsuite/btcd/btcec#example-package--DecryptMessage) Demonstrates decrypting a message using a private key that is first parsed from raw bytes. diff --git a/vendor/github.com/btcsuite/btcd/btcec/btcec.go b/vendor/github.com/btcsuite/btcd/btcec/btcec.go index de93a25..a2e20f4 100644 --- a/vendor/github.com/btcsuite/btcd/btcec/btcec.go +++ b/vendor/github.com/btcsuite/btcd/btcec/btcec.go @@ -930,6 +930,8 @@ func initS256() { secp256k1.Gx = fromHex("79BE667EF9DCBBAC55A06295CE870B07029BFCDB2DCE28D959F2815B16F81798") secp256k1.Gy = fromHex("483ADA7726A3C4655DA4FBFC0E1108A8FD17B448A68554199C47D08FFB10D4B8") secp256k1.BitSize = 256 + // Curve name taken from https://safecurves.cr.yp.to/. + secp256k1.Name = "secp256k1" secp256k1.q = new(big.Int).Div(new(big.Int).Add(secp256k1.P, big.NewInt(1)), big.NewInt(4)) secp256k1.H = 1 diff --git a/vendor/github.com/btcsuite/btcd/btcec/signature.go b/vendor/github.com/btcsuite/btcd/btcec/signature.go index deedd17..cdd7ced 100644 --- a/vendor/github.com/btcsuite/btcd/btcec/signature.go +++ b/vendor/github.com/btcsuite/btcd/btcec/signature.go @@ -284,6 +284,25 @@ func hashToInt(hash []byte, c elliptic.Curve) *big.Int { // format and thus we match bitcoind's behaviour here. func recoverKeyFromSignature(curve *KoblitzCurve, sig *Signature, msg []byte, iter int, doChecks bool) (*PublicKey, error) { + // Parse and validate the R and S signature components. + // + // Fail if r and s are not in [1, N-1]. + if sig.R.Cmp(curve.Params().N) != -1 { + return nil, errors.New("signature R is >= curve order") + } + + if sig.R.Sign() == 0 { + return nil, errors.New("signature R is 0") + } + + if sig.S.Cmp(curve.Params().N) != -1 { + return nil, errors.New("signature S is >= curve order") + } + + if sig.S.Sign() == 0 { + return nil, errors.New("signature S is 0") + } + // 1.1 x = (n * i) + r Rx := new(big.Int).Mul(curve.Params().N, new(big.Int).SetInt64(int64(iter/2))) @@ -393,7 +412,7 @@ func SignCompact(curve *KoblitzCurve, key *PrivateKey, // RecoverCompact verifies the compact signature "signature" of "hash" for the // Koblitz curve in "curve". If the signature matches then the recovered public -// key will be returned as well as a boolen if the original key was compressed +// key will be returned as well as a boolean if the original key was compressed // or not, else an error will be returned. func RecoverCompact(curve *KoblitzCurve, signature, hash []byte) (*PublicKey, bool, error) { diff --git a/vendor/github.com/btcsuite/btcd/btcec/v2/LICENSE b/vendor/github.com/btcsuite/btcd/btcec/v2/LICENSE new file mode 100644 index 0000000..23190ba --- /dev/null +++ b/vendor/github.com/btcsuite/btcd/btcec/v2/LICENSE @@ -0,0 +1,16 @@ +ISC License + +Copyright (c) 2013-2022 The btcsuite developers +Copyright (c) 2015-2016 The Decred developers + +Permission to use, copy, modify, and distribute this software for any +purpose with or without fee is hereby granted, provided that the above +copyright notice and this permission notice appear in all copies. + +THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES +WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF +MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR +ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES +WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN +ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF +OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. diff --git a/vendor/github.com/btcsuite/btcd/btcec/v2/README.md b/vendor/github.com/btcsuite/btcd/btcec/v2/README.md new file mode 100644 index 0000000..cbf63dd --- /dev/null +++ b/vendor/github.com/btcsuite/btcd/btcec/v2/README.md @@ -0,0 +1,40 @@ +btcec +===== + +[![Build Status](https://github.com/btcsuite/btcd/workflows/Build%20and%20Test/badge.svg)](https://github.com/btcsuite/btcd/actions) +[![ISC License](http://img.shields.io/badge/license-ISC-blue.svg)](http://copyfree.org) +[![GoDoc](https://pkg.go.dev/github.com/btcsuite/btcd/btcec/v2?status.png)](https://pkg.go.dev/github.com/btcsuite/btcd/btcec/v2) + +Package btcec implements elliptic curve cryptography needed for working with +Bitcoin (secp256k1 only for now). It is designed so that it may be used with the +standard crypto/ecdsa packages provided with go. A comprehensive suite of test +is provided to ensure proper functionality. Package btcec was originally based +on work from ThePiachu which is licensed under the same terms as Go, but it has +signficantly diverged since then. The btcsuite developers original is licensed +under the liberal ISC license. + +Although this package was primarily written for btcd, it has intentionally been +designed so it can be used as a standalone package for any projects needing to +use secp256k1 elliptic curve cryptography. + +## Installation and Updating + +```bash +$ go install -u -v github.com/btcsuite/btcd/btcec/v2 +``` + +## Examples + +* [Sign Message](https://pkg.go.dev/github.com/btcsuite/btcd/btcec/v2#example-package--SignMessage) + Demonstrates signing a message with a secp256k1 private key that is first + parsed form raw bytes and serializing the generated signature. + +* [Verify Signature](https://pkg.go.dev/github.com/btcsuite/btcd/btcec/v2#example-package--VerifySignature) + Demonstrates verifying a secp256k1 signature against a public key that is + first parsed from raw bytes. The signature is also parsed from raw bytes. + +## License + +Package btcec is licensed under the [copyfree](http://copyfree.org) ISC License +except for btcec.go and btcec_test.go which is under the same license as Go. + diff --git a/vendor/github.com/btcsuite/btcd/btcec/v2/btcec.go b/vendor/github.com/btcsuite/btcd/btcec/v2/btcec.go new file mode 100644 index 0000000..efde8d6 --- /dev/null +++ b/vendor/github.com/btcsuite/btcd/btcec/v2/btcec.go @@ -0,0 +1,41 @@ +// Copyright 2010 The Go Authors. All rights reserved. +// Copyright 2011 ThePiachu. All rights reserved. +// Copyright 2013-2014 The btcsuite developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +package btcec + +// References: +// [SECG]: Recommended Elliptic Curve Domain Parameters +// http://www.secg.org/sec2-v2.pdf +// +// [GECC]: Guide to Elliptic Curve Cryptography (Hankerson, Menezes, Vanstone) + +// This package operates, internally, on Jacobian coordinates. For a given +// (x, y) position on the curve, the Jacobian coordinates are (x1, y1, z1) +// where x = x1/z1² and y = y1/z1³. The greatest speedups come when the whole +// calculation can be performed within the transform (as in ScalarMult and +// ScalarBaseMult). But even for Add and Double, it's faster to apply and +// reverse the transform than to operate in affine coordinates. + +import ( + secp "github.com/decred/dcrd/dcrec/secp256k1/v4" +) + +// KoblitzCurve provides an implementation for secp256k1 that fits the ECC +// Curve interface from crypto/elliptic. +type KoblitzCurve = secp.KoblitzCurve + +// S256 returns a Curve which implements secp256k1. +func S256() *KoblitzCurve { + return secp.S256() +} + +// CurveParams contains the parameters for the secp256k1 curve. +type CurveParams = secp.CurveParams + +// Params returns the secp256k1 curve parameters for convenience. +func Params() *CurveParams { + return secp.Params() +} diff --git a/vendor/github.com/btcsuite/btcd/btcec/v2/ciphering.go b/vendor/github.com/btcsuite/btcd/btcec/v2/ciphering.go new file mode 100644 index 0000000..88d93e2 --- /dev/null +++ b/vendor/github.com/btcsuite/btcd/btcec/v2/ciphering.go @@ -0,0 +1,16 @@ +// Copyright (c) 2015-2016 The btcsuite developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +package btcec + +import ( + secp "github.com/decred/dcrd/dcrec/secp256k1/v4" +) + +// GenerateSharedSecret generates a shared secret based on a private key and a +// public key using Diffie-Hellman key exchange (ECDH) (RFC 4753). +// RFC5903 Section 9 states we should only return x. +func GenerateSharedSecret(privkey *PrivateKey, pubkey *PublicKey) []byte { + return secp.GenerateSharedSecret(privkey, pubkey) +} diff --git a/vendor/github.com/btcsuite/btcd/btcec/v2/curve.go b/vendor/github.com/btcsuite/btcd/btcec/v2/curve.go new file mode 100644 index 0000000..5224e35 --- /dev/null +++ b/vendor/github.com/btcsuite/btcd/btcec/v2/curve.go @@ -0,0 +1,63 @@ +// Copyright (c) 2015-2021 The btcsuite developers +// Copyright (c) 2015-2021 The Decred developers + +package btcec + +import ( + secp "github.com/decred/dcrd/dcrec/secp256k1/v4" +) + +// JacobianPoint is an element of the group formed by the secp256k1 curve in +// Jacobian projective coordinates and thus represents a point on the curve. +type JacobianPoint = secp.JacobianPoint + +// MakeJacobianPoint returns a Jacobian point with the provided X, Y, and Z +// coordinates. +func MakeJacobianPoint(x, y, z *FieldVal) JacobianPoint { + return secp.MakeJacobianPoint(x, y, z) +} + +// AddNonConst adds the passed Jacobian points together and stores the result +// in the provided result param in *non-constant* time. +func AddNonConst(p1, p2, result *JacobianPoint) { + secp.AddNonConst(p1, p2, result) +} + +// DecompressY attempts to calculate the Y coordinate for the given X +// coordinate such that the result pair is a point on the secp256k1 curve. It +// adjusts Y based on the desired oddness and returns whether or not it was +// successful since not all X coordinates are valid. +// +// The magnitude of the provided X coordinate field val must be a max of 8 for +// a correct result. The resulting Y field val will have a max magnitude of 2. +func DecompressY(x *FieldVal, odd bool, resultY *FieldVal) bool { + return secp.DecompressY(x, odd, resultY) +} + +// DoubleNonConst doubles the passed Jacobian point and stores the result in +// the provided result parameter in *non-constant* time. +// +// NOTE: The point must be normalized for this function to return the correct +// result. The resulting point will be normalized. +func DoubleNonConst(p, result *JacobianPoint) { + secp.DoubleNonConst(p, result) +} + +// ScalarBaseMultNonConst multiplies k*G where G is the base point of the group +// and k is a big endian integer. The result is stored in Jacobian coordinates +// (x1, y1, z1). +// +// NOTE: The resulting point will be normalized. +func ScalarBaseMultNonConst(k *ModNScalar, result *JacobianPoint) { + secp.ScalarBaseMultNonConst(k, result) +} + +// ScalarMultNonConst multiplies k*P where k is a big endian integer modulo the +// curve order and P is a point in Jacobian projective coordinates and stores +// the result in the provided Jacobian point. +// +// NOTE: The point must be normalized for this function to return the correct +// result. The resulting point will be normalized. +func ScalarMultNonConst(k *ModNScalar, point, result *JacobianPoint) { + secp.ScalarMultNonConst(k, point, result) +} diff --git a/vendor/github.com/btcsuite/btcd/btcec/v2/doc.go b/vendor/github.com/btcsuite/btcd/btcec/v2/doc.go new file mode 100644 index 0000000..fa8346a --- /dev/null +++ b/vendor/github.com/btcsuite/btcd/btcec/v2/doc.go @@ -0,0 +1,21 @@ +// Copyright (c) 2013-2014 The btcsuite developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +/* +Package btcec implements support for the elliptic curves needed for bitcoin. + +Bitcoin uses elliptic curve cryptography using koblitz curves +(specifically secp256k1) for cryptographic functions. See +http://www.secg.org/collateral/sec2_final.pdf for details on the +standard. + +This package provides the data structures and functions implementing the +crypto/elliptic Curve interface in order to permit using these curves +with the standard crypto/ecdsa package provided with go. Helper +functionality is provided to parse signatures and public keys from +standard formats. It was designed for use with btcd, but should be +general enough for other uses of elliptic curve crypto. It was originally based +on some initial work by ThePiachu, but has significantly diverged since then. +*/ +package btcec diff --git a/vendor/github.com/btcsuite/btcd/btcec/v2/ecdsa/error.go b/vendor/github.com/btcsuite/btcd/btcec/v2/ecdsa/error.go new file mode 100644 index 0000000..a30d63b --- /dev/null +++ b/vendor/github.com/btcsuite/btcd/btcec/v2/ecdsa/error.go @@ -0,0 +1,18 @@ +// Copyright (c) 2013-2021 The btcsuite developers +// Copyright (c) 2015-2021 The Decred developers + +package ecdsa + +import ( + secp_ecdsa "github.com/decred/dcrd/dcrec/secp256k1/v4/ecdsa" +) + +// ErrorKind identifies a kind of error. It has full support for +// errors.Is and errors.As, so the caller can directly check against +// an error kind when determining the reason for an error. +type ErrorKind = secp_ecdsa.ErrorKind + +// Error identifies an error related to an ECDSA signature. It has full +// support for errors.Is and errors.As, so the caller can ascertain the +// specific reason for the error by checking the underlying error. +type Error = secp_ecdsa.ErrorKind diff --git a/vendor/github.com/btcsuite/btcd/btcec/v2/ecdsa/signature.go b/vendor/github.com/btcsuite/btcd/btcec/v2/ecdsa/signature.go new file mode 100644 index 0000000..092e4ce --- /dev/null +++ b/vendor/github.com/btcsuite/btcd/btcec/v2/ecdsa/signature.go @@ -0,0 +1,240 @@ +// Copyright (c) 2013-2017 The btcsuite developers +// Copyright (c) 2015-2021 The Decred developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +package ecdsa + +import ( + "errors" + "fmt" + "math/big" + + "github.com/btcsuite/btcd/btcec/v2" + secp_ecdsa "github.com/decred/dcrd/dcrec/secp256k1/v4/ecdsa" +) + +// Errors returned by canonicalPadding. +var ( + errNegativeValue = errors.New("value may be interpreted as negative") + errExcessivelyPaddedValue = errors.New("value is excessively padded") +) + +// Signature is a type representing an ecdsa signature. +type Signature = secp_ecdsa.Signature + +// NewSignature instantiates a new signature given some r and s values. +func NewSignature(r, s *btcec.ModNScalar) *Signature { + return secp_ecdsa.NewSignature(r, s) +} + +var ( + // Used in RFC6979 implementation when testing the nonce for correctness + one = big.NewInt(1) + + // oneInitializer is used to fill a byte slice with byte 0x01. It is provided + // here to avoid the need to create it multiple times. + oneInitializer = []byte{0x01} +) + +// MinSigLen is the minimum length of a DER encoded signature and is when both R +// and S are 1 byte each. +// 0x30 + <1-byte> + 0x02 + 0x01 + + 0x2 + 0x01 + +const MinSigLen = 8 + +// canonicalPadding checks whether a big-endian encoded integer could +// possibly be misinterpreted as a negative number (even though OpenSSL +// treats all numbers as unsigned), or if there is any unnecessary +// leading zero padding. +func canonicalPadding(b []byte) error { + switch { + case b[0]&0x80 == 0x80: + return errNegativeValue + case len(b) > 1 && b[0] == 0x00 && b[1]&0x80 != 0x80: + return errExcessivelyPaddedValue + default: + return nil + } +} + +func parseSig(sigStr []byte, der bool) (*Signature, error) { + // Originally this code used encoding/asn1 in order to parse the + // signature, but a number of problems were found with this approach. + // Despite the fact that signatures are stored as DER, the difference + // between go's idea of a bignum (and that they have sign) doesn't agree + // with the openssl one (where they do not). The above is true as of + // Go 1.1. In the end it was simpler to rewrite the code to explicitly + // understand the format which is this: + // 0x30 <0x02> 0x2 + // . + + if len(sigStr) < MinSigLen { + return nil, errors.New("malformed signature: too short") + } + // 0x30 + index := 0 + if sigStr[index] != 0x30 { + return nil, errors.New("malformed signature: no header magic") + } + index++ + // length of remaining message + siglen := sigStr[index] + index++ + + // siglen should be less than the entire message and greater than + // the minimal message size. + if int(siglen+2) > len(sigStr) || int(siglen+2) < MinSigLen { + return nil, errors.New("malformed signature: bad length") + } + // trim the slice we're working on so we only look at what matters. + sigStr = sigStr[:siglen+2] + + // 0x02 + if sigStr[index] != 0x02 { + return nil, + errors.New("malformed signature: no 1st int marker") + } + index++ + + // Length of signature R. + rLen := int(sigStr[index]) + // must be positive, must be able to fit in another 0x2, + // hence the -3. We assume that the length must be at least one byte. + index++ + if rLen <= 0 || rLen > len(sigStr)-index-3 { + return nil, errors.New("malformed signature: bogus R length") + } + + // Then R itself. + rBytes := sigStr[index : index+rLen] + if der { + switch err := canonicalPadding(rBytes); err { + case errNegativeValue: + return nil, errors.New("signature R is negative") + case errExcessivelyPaddedValue: + return nil, errors.New("signature R is excessively padded") + } + } + + // Strip leading zeroes from R. + for len(rBytes) > 0 && rBytes[0] == 0x00 { + rBytes = rBytes[1:] + } + + // R must be in the range [1, N-1]. Notice the check for the maximum number + // of bytes is required because SetByteSlice truncates as noted in its + // comment so it could otherwise fail to detect the overflow. + var r btcec.ModNScalar + if len(rBytes) > 32 { + str := "invalid signature: R is larger than 256 bits" + return nil, errors.New(str) + } + if overflow := r.SetByteSlice(rBytes); overflow { + str := "invalid signature: R >= group order" + return nil, errors.New(str) + } + if r.IsZero() { + str := "invalid signature: R is 0" + return nil, errors.New(str) + } + index += rLen + // 0x02. length already checked in previous if. + if sigStr[index] != 0x02 { + return nil, errors.New("malformed signature: no 2nd int marker") + } + index++ + + // Length of signature S. + sLen := int(sigStr[index]) + index++ + // S should be the rest of the string. + if sLen <= 0 || sLen > len(sigStr)-index { + return nil, errors.New("malformed signature: bogus S length") + } + + // Then S itself. + sBytes := sigStr[index : index+sLen] + if der { + switch err := canonicalPadding(sBytes); err { + case errNegativeValue: + return nil, errors.New("signature S is negative") + case errExcessivelyPaddedValue: + return nil, errors.New("signature S is excessively padded") + } + } + + // Strip leading zeroes from S. + for len(sBytes) > 0 && sBytes[0] == 0x00 { + sBytes = sBytes[1:] + } + + // S must be in the range [1, N-1]. Notice the check for the maximum number + // of bytes is required because SetByteSlice truncates as noted in its + // comment so it could otherwise fail to detect the overflow. + var s btcec.ModNScalar + if len(sBytes) > 32 { + str := "invalid signature: S is larger than 256 bits" + return nil, errors.New(str) + } + if overflow := s.SetByteSlice(sBytes); overflow { + str := "invalid signature: S >= group order" + return nil, errors.New(str) + } + if s.IsZero() { + str := "invalid signature: S is 0" + return nil, errors.New(str) + } + index += sLen + + // sanity check length parsing + if index != len(sigStr) { + return nil, fmt.Errorf("malformed signature: bad final length %v != %v", + index, len(sigStr)) + } + + return NewSignature(&r, &s), nil +} + +// ParseSignature parses a signature in BER format for the curve type `curve' +// into a Signature type, perfoming some basic sanity checks. If parsing +// according to the more strict DER format is needed, use ParseDERSignature. +func ParseSignature(sigStr []byte) (*Signature, error) { + return parseSig(sigStr, false) +} + +// ParseDERSignature parses a signature in DER format for the curve type +// `curve` into a Signature type. If parsing according to the less strict +// BER format is needed, use ParseSignature. +func ParseDERSignature(sigStr []byte) (*Signature, error) { + return parseSig(sigStr, true) +} + +// SignCompact produces a compact signature of the data in hash with the given +// private key on the given koblitz curve. The isCompressed parameter should +// be used to detail if the given signature should reference a compressed +// public key or not. If successful the bytes of the compact signature will be +// returned in the format: +// <(byte of 27+public key solution)+4 if compressed >< padded bytes for signature R> +// where the R and S parameters are padde up to the bitlengh of the curve. +func SignCompact(key *btcec.PrivateKey, hash []byte, + isCompressedKey bool) ([]byte, error) { + + return secp_ecdsa.SignCompact(key, hash, isCompressedKey), nil +} + +// RecoverCompact verifies the compact signature "signature" of "hash" for the +// Koblitz curve in "curve". If the signature matches then the recovered public +// key will be returned as well as a boolean if the original key was compressed +// or not, else an error will be returned. +func RecoverCompact(signature, hash []byte) (*btcec.PublicKey, bool, error) { + return secp_ecdsa.RecoverCompact(signature, hash) +} + +// Sign generates an ECDSA signature over the secp256k1 curve for the provided +// hash (which should be the result of hashing a larger message) using the +// given private key. The produced signature is deterministic (same message and +// same key yield the same signature) and canonical in accordance with RFC6979 +// and BIP0062. +func Sign(key *btcec.PrivateKey, hash []byte) *Signature { + return secp_ecdsa.Sign(key, hash) +} diff --git a/vendor/github.com/btcsuite/btcd/btcec/v2/error.go b/vendor/github.com/btcsuite/btcd/btcec/v2/error.go new file mode 100644 index 0000000..81ca2b0 --- /dev/null +++ b/vendor/github.com/btcsuite/btcd/btcec/v2/error.go @@ -0,0 +1,19 @@ +// Copyright (c) 2013-2021 The btcsuite developers +// Copyright (c) 2015-2021 The Decred developers + +package btcec + +import ( + secp "github.com/decred/dcrd/dcrec/secp256k1/v4" +) + +// Error identifies an error related to public key cryptography using a +// sec256k1 curve. It has full support for errors.Is and errors.As, so the +// caller can ascertain the specific reason for the error by checking the +// underlying error. +type Error = secp.Error + +// ErrorKind identifies a kind of error. It has full support for errors.Is and +// errors.As, so the caller can directly check against an error kind when +// determining the reason for an error. +type ErrorKind = secp.ErrorKind diff --git a/vendor/github.com/btcsuite/btcd/btcec/v2/field.go b/vendor/github.com/btcsuite/btcd/btcec/v2/field.go new file mode 100644 index 0000000..fef6f34 --- /dev/null +++ b/vendor/github.com/btcsuite/btcd/btcec/v2/field.go @@ -0,0 +1,43 @@ +package btcec + +import secp "github.com/decred/dcrd/dcrec/secp256k1/v4" + +// FieldVal implements optimized fixed-precision arithmetic over the secp256k1 +// finite field. This means all arithmetic is performed modulo +// '0xfffffffffffffffffffffffffffffffffffffffffffffffffffffffefffffc2f'. +// +// WARNING: Since it is so important for the field arithmetic to be extremely +// fast for high performance crypto, this type does not perform any validation +// of documented preconditions where it ordinarily would. As a result, it is +// IMPERATIVE for callers to understand some key concepts that are described +// below and ensure the methods are called with the necessary preconditions +// that each method is documented with. For example, some methods only give the +// correct result if the field value is normalized and others require the field +// values involved to have a maximum magnitude and THERE ARE NO EXPLICIT CHECKS +// TO ENSURE THOSE PRECONDITIONS ARE SATISFIED. This does, unfortunately, make +// the type more difficult to use correctly and while I typically prefer to +// ensure all state and input is valid for most code, this is a bit of an +// exception because those extra checks really add up in what ends up being +// critical hot paths. +// +// The first key concept when working with this type is normalization. In order +// to avoid the need to propagate a ton of carries, the internal representation +// provides additional overflow bits for each word of the overall 256-bit +// value. This means that there are multiple internal representations for the +// same value and, as a result, any methods that rely on comparison of the +// value, such as equality and oddness determination, require the caller to +// provide a normalized value. +// +// The second key concept when working with this type is magnitude. As +// previously mentioned, the internal representation provides additional +// overflow bits which means that the more math operations that are performed +// on the field value between normalizations, the more those overflow bits +// accumulate. The magnitude is effectively that maximum possible number of +// those overflow bits that could possibly be required as a result of a given +// operation. Since there are only a limited number of overflow bits available, +// this implies that the max possible magnitude MUST be tracked by the caller +// and the caller MUST normalize the field value if a given operation would +// cause the magnitude of the result to exceed the max allowed value. +// +// IMPORTANT: The max allowed magnitude of a field value is 64. +type FieldVal = secp.FieldVal diff --git a/vendor/github.com/btcsuite/btcd/btcec/v2/modnscalar.go b/vendor/github.com/btcsuite/btcd/btcec/v2/modnscalar.go new file mode 100644 index 0000000..b18b2c1 --- /dev/null +++ b/vendor/github.com/btcsuite/btcd/btcec/v2/modnscalar.go @@ -0,0 +1,45 @@ +// Copyright (c) 2013-2021 The btcsuite developers +// Copyright (c) 2015-2021 The Decred developers + +package btcec + +import ( + secp "github.com/decred/dcrd/dcrec/secp256k1/v4" +) + +// ModNScalar implements optimized 256-bit constant-time fixed-precision +// arithmetic over the secp256k1 group order. This means all arithmetic is +// performed modulo: +// +// 0xfffffffffffffffffffffffffffffffebaaedce6af48a03bbfd25e8cd0364141 +// +// It only implements the arithmetic needed for elliptic curve operations, +// however, the operations that are not implemented can typically be worked +// around if absolutely needed. For example, subtraction can be performed by +// adding the negation. +// +// Should it be absolutely necessary, conversion to the standard library +// math/big.Int can be accomplished by using the Bytes method, slicing the +// resulting fixed-size array, and feeding it to big.Int.SetBytes. However, +// that should typically be avoided when possible as conversion to big.Ints +// requires allocations, is not constant time, and is slower when working modulo +// the group order. +type ModNScalar = secp.ModNScalar + +// NonceRFC6979 generates a nonce deterministically according to RFC 6979 using +// HMAC-SHA256 for the hashing function. It takes a 32-byte hash as an input +// and returns a 32-byte nonce to be used for deterministic signing. The extra +// and version arguments are optional, but allow additional data to be added to +// the input of the HMAC. When provided, the extra data must be 32-bytes and +// version must be 16 bytes or they will be ignored. +// +// Finally, the extraIterations parameter provides a method to produce a stream +// of deterministic nonces to ensure the signing code is able to produce a nonce +// that results in a valid signature in the extremely unlikely event the +// original nonce produced results in an invalid signature (e.g. R == 0). +// Signing code should start with 0 and increment it if necessary. +func NonceRFC6979(privKey []byte, hash []byte, extra []byte, version []byte, + extraIterations uint32) *ModNScalar { + + return secp.NonceRFC6979(privKey, hash, extra, version, extraIterations) +} diff --git a/vendor/github.com/btcsuite/btcd/btcec/v2/privkey.go b/vendor/github.com/btcsuite/btcd/btcec/v2/privkey.go new file mode 100644 index 0000000..4efa806 --- /dev/null +++ b/vendor/github.com/btcsuite/btcd/btcec/v2/privkey.go @@ -0,0 +1,37 @@ +// Copyright (c) 2013-2016 The btcsuite developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +package btcec + +import ( + secp "github.com/decred/dcrd/dcrec/secp256k1/v4" +) + +// PrivateKey wraps an ecdsa.PrivateKey as a convenience mainly for signing +// things with the the private key without having to directly import the ecdsa +// package. +type PrivateKey = secp.PrivateKey + +// PrivKeyFromBytes returns a private and public key for `curve' based on the +// private key passed as an argument as a byte slice. +func PrivKeyFromBytes(pk []byte) (*PrivateKey, *PublicKey) { + privKey := secp.PrivKeyFromBytes(pk) + + return privKey, privKey.PubKey() +} + +// NewPrivateKey is a wrapper for ecdsa.GenerateKey that returns a PrivateKey +// instead of the normal ecdsa.PrivateKey. +func NewPrivateKey() (*PrivateKey, error) { + return secp.GeneratePrivateKey() +} + +// PrivKeyFromScalar instantiates a new private key from a scalar encoded as a +// big integer. +func PrivKeyFromScalar(key *ModNScalar) *PrivateKey { + return &PrivateKey{Key: *key} +} + +// PrivKeyBytesLen defines the length in bytes of a serialized private key. +const PrivKeyBytesLen = 32 diff --git a/vendor/github.com/btcsuite/btcd/btcec/v2/pubkey.go b/vendor/github.com/btcsuite/btcd/btcec/v2/pubkey.go new file mode 100644 index 0000000..7968ed0 --- /dev/null +++ b/vendor/github.com/btcsuite/btcd/btcec/v2/pubkey.go @@ -0,0 +1,51 @@ +// Copyright (c) 2013-2014 The btcsuite developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +package btcec + +import ( + secp "github.com/decred/dcrd/dcrec/secp256k1/v4" +) + +// These constants define the lengths of serialized public keys. +const ( + PubKeyBytesLenCompressed = 33 +) + +const ( + pubkeyCompressed byte = 0x2 // y_bit + x coord + pubkeyUncompressed byte = 0x4 // x coord + y coord + pubkeyHybrid byte = 0x6 // y_bit + x coord + y coord +) + +// IsCompressedPubKey returns true the the passed serialized public key has +// been encoded in compressed format, and false otherwise. +func IsCompressedPubKey(pubKey []byte) bool { + // The public key is only compressed if it is the correct length and + // the format (first byte) is one of the compressed pubkey values. + return len(pubKey) == PubKeyBytesLenCompressed && + (pubKey[0]&^byte(0x1) == pubkeyCompressed) +} + +// ParsePubKey parses a public key for a koblitz curve from a bytestring into a +// ecdsa.Publickey, verifying that it is valid. It supports compressed, +// uncompressed and hybrid signature formats. +func ParsePubKey(pubKeyStr []byte) (*PublicKey, error) { + return secp.ParsePubKey(pubKeyStr) +} + +// PublicKey is an ecdsa.PublicKey with additional functions to +// serialize in uncompressed, compressed, and hybrid formats. +type PublicKey = secp.PublicKey + +// NewPublicKey instantiates a new public key with the given x and y +// coordinates. +// +// It should be noted that, unlike ParsePubKey, since this accepts arbitrary x +// and y coordinates, it allows creation of public keys that are not valid +// points on the secp256k1 curve. The IsOnCurve method of the returned instance +// can be used to determine validity. +func NewPublicKey(x, y *FieldVal) *PublicKey { + return secp.NewPublicKey(x, y) +} diff --git a/vendor/github.com/btcsuite/btcd/chaincfg/README.md b/vendor/github.com/btcsuite/btcd/chaincfg/README.md index 880446f..72fac2e 100644 --- a/vendor/github.com/btcsuite/btcd/chaincfg/README.md +++ b/vendor/github.com/btcsuite/btcd/chaincfg/README.md @@ -1,9 +1,9 @@ chaincfg ======== -[![Build Status](http://img.shields.io/travis/btcsuite/btcd.svg)](https://travis-ci.org/btcsuite/btcd) +[![Build Status](https://github.com/btcsuite/btcd/workflows/Build%20and%20Test/badge.svg)](https://github.com/btcsuite/btcd/actions) [![ISC License](http://img.shields.io/badge/license-ISC-blue.svg)](http://copyfree.org) -[![GoDoc](https://img.shields.io/badge/godoc-reference-blue.svg)](http://godoc.org/github.com/btcsuite/btcd/chaincfg) +[![GoDoc](https://img.shields.io/badge/godoc-reference-blue.svg)](https://pkg.go.dev/github.com/btcsuite/btcd/chaincfg) Package chaincfg defines chain configuration parameters for the three standard Bitcoin networks and provides the ability for callers to define their own custom diff --git a/vendor/github.com/btcsuite/btcd/chaincfg/chainhash/LICENSE b/vendor/github.com/btcsuite/btcd/chaincfg/chainhash/LICENSE new file mode 100644 index 0000000..23190ba --- /dev/null +++ b/vendor/github.com/btcsuite/btcd/chaincfg/chainhash/LICENSE @@ -0,0 +1,16 @@ +ISC License + +Copyright (c) 2013-2022 The btcsuite developers +Copyright (c) 2015-2016 The Decred developers + +Permission to use, copy, modify, and distribute this software for any +purpose with or without fee is hereby granted, provided that the above +copyright notice and this permission notice appear in all copies. + +THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES +WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF +MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR +ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES +WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN +ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF +OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. diff --git a/vendor/github.com/btcsuite/btcd/chaincfg/chainhash/README.md b/vendor/github.com/btcsuite/btcd/chaincfg/chainhash/README.md index fc49d9c..b7ddf19 100644 --- a/vendor/github.com/btcsuite/btcd/chaincfg/chainhash/README.md +++ b/vendor/github.com/btcsuite/btcd/chaincfg/chainhash/README.md @@ -1,9 +1,9 @@ chainhash ========= -[![Build Status](http://img.shields.io/travis/btcsuite/btcd.svg)](https://travis-ci.org/btcsuite/btcd) +[![Build Status](https://github.com/btcsuite/btcd/workflows/Build%20and%20Test/badge.svg)](https://github.com/btcsuite/btcd/actions) [![ISC License](http://img.shields.io/badge/license-ISC-blue.svg)](http://copyfree.org) -[![GoDoc](https://img.shields.io/badge/godoc-reference-blue.svg)](http://godoc.org/github.com/btcsuite/btcd/chaincfg/chainhash) +[![GoDoc](https://img.shields.io/badge/godoc-reference-blue.svg)](https://pkg.go.dev/github.com/btcsuite/btcd/chaincfg/chainhash) ======= chainhash provides a generic hash type and associated functions that allows the diff --git a/vendor/github.com/btcsuite/btcd/chaincfg/chainhash/hash.go b/vendor/github.com/btcsuite/btcd/chaincfg/chainhash/hash.go index 2b1cec0..764ec3c 100644 --- a/vendor/github.com/btcsuite/btcd/chaincfg/chainhash/hash.go +++ b/vendor/github.com/btcsuite/btcd/chaincfg/chainhash/hash.go @@ -6,6 +6,7 @@ package chainhash import ( + "crypto/sha256" "encoding/hex" "fmt" ) @@ -16,6 +17,46 @@ const HashSize = 32 // MaxHashStringSize is the maximum length of a Hash hash string. const MaxHashStringSize = HashSize * 2 +var ( + // TagBIP0340Challenge is the BIP-0340 tag for challenges. + TagBIP0340Challenge = []byte("BIP0340/challenge") + + // TagBIP0340Aux is the BIP-0340 tag for aux data. + TagBIP0340Aux = []byte("BIP0340/aux") + + // TagBIP0340Nonce is the BIP-0340 tag for nonces. + TagBIP0340Nonce = []byte("BIP0340/nonce") + + // TagTapSighash is the tag used by BIP 341 to generate the sighash + // flags. + TagTapSighash = []byte("TapSighash") + + // TagTagTapLeaf is the message tag prefix used to compute the hash + // digest of a tapscript leaf. + TagTapLeaf = []byte("TapLeaf") + + // TagTapBranch is the message tag prefix used to compute the + // hash digest of two tap leaves into a taproot branch node. + TagTapBranch = []byte("TapBranch") + + // TagTapTweak is the message tag prefix used to compute the hash tweak + // used to enable a public key to commit to the taproot branch root + // for the witness program. + TagTapTweak = []byte("TapTweak") + + // precomputedTags is a map containing the SHA-256 hash of the BIP-0340 + // tags. + precomputedTags = map[string]Hash{ + string(TagBIP0340Challenge): sha256.Sum256(TagBIP0340Challenge), + string(TagBIP0340Aux): sha256.Sum256(TagBIP0340Aux), + string(TagBIP0340Nonce): sha256.Sum256(TagBIP0340Nonce), + string(TagTapSighash): sha256.Sum256(TagTapSighash), + string(TagTapLeaf): sha256.Sum256(TagTapLeaf), + string(TagTapBranch): sha256.Sum256(TagTapBranch), + string(TagTapTweak): sha256.Sum256(TagTapTweak), + } +) + // ErrHashStrSize describes an error that indicates the caller specified a hash // string that has too many characters. var ErrHashStrSize = fmt.Errorf("max hash string length is %v bytes", MaxHashStringSize) @@ -80,6 +121,35 @@ func NewHash(newHash []byte) (*Hash, error) { return &sh, err } +// TaggedHash implements the tagged hash scheme described in BIP-340. We use +// sha-256 to bind a message hash to a specific context using a tag: +// sha256(sha256(tag) || sha256(tag) || msg). +func TaggedHash(tag []byte, msgs ...[]byte) *Hash { + // Check to see if we've already pre-computed the hash of the tag. If + // so then this'll save us an extra sha256 hash. + shaTag, ok := precomputedTags[string(tag)] + if !ok { + shaTag = sha256.Sum256(tag) + } + + // h = sha256(sha256(tag) || sha256(tag) || msg) + h := sha256.New() + h.Write(shaTag[:]) + h.Write(shaTag[:]) + + for _, msg := range msgs { + h.Write(msg) + } + + taggedHash := h.Sum(nil) + + // The function can't error out since the above hash is guaranteed to + // be 32 bytes. + hash, _ := NewHash(taggedHash) + + return hash +} + // NewHashFromStr creates a Hash from a hash string. The string should be // the hexadecimal string of a byte-reversed hash, but any missing characters // result in zero padding at the end of the Hash. diff --git a/vendor/github.com/btcsuite/btcd/chaincfg/genesis.go b/vendor/github.com/btcsuite/btcd/chaincfg/genesis.go index ee47b84..73d2861 100644 --- a/vendor/github.com/btcsuite/btcd/chaincfg/genesis.go +++ b/vendor/github.com/btcsuite/btcd/chaincfg/genesis.go @@ -170,3 +170,31 @@ var simNetGenesisBlock = wire.MsgBlock{ }, Transactions: []*wire.MsgTx{&genesisCoinbaseTx}, } + +// sigNetGenesisHash is the hash of the first block in the block chain for the +// signet test network. +var sigNetGenesisHash = chainhash.Hash{ + 0xf6, 0x1e, 0xee, 0x3b, 0x63, 0xa3, 0x80, 0xa4, + 0x77, 0xa0, 0x63, 0xaf, 0x32, 0xb2, 0xbb, 0xc9, + 0x7c, 0x9f, 0xf9, 0xf0, 0x1f, 0x2c, 0x42, 0x25, + 0xe9, 0x73, 0x98, 0x81, 0x08, 0x00, 0x00, 0x00, +} + +// sigNetGenesisMerkleRoot is the hash of the first transaction in the genesis +// block for the signet test network. It is the same as the merkle root for +// the main network. +var sigNetGenesisMerkleRoot = genesisMerkleRoot + +// sigNetGenesisBlock defines the genesis block of the block chain which serves +// as the public transaction ledger for the signet test network. +var sigNetGenesisBlock = wire.MsgBlock{ + Header: wire.BlockHeader{ + Version: 1, + PrevBlock: chainhash.Hash{}, // 0000000000000000000000000000000000000000000000000000000000000000 + MerkleRoot: sigNetGenesisMerkleRoot, // 4a5e1e4baab89f3a32518a88c31bc87f618f76673e2cc77ab2127b7afdeda33b + Timestamp: time.Unix(1598918400, 0), // 2020-09-01 00:00:00 +0000 UTC + Bits: 0x1e0377ae, // 503543726 [00000377ae000000000000000000000000000000000000000000000000000000] + Nonce: 52613770, + }, + Transactions: []*wire.MsgTx{&genesisCoinbaseTx}, +} diff --git a/vendor/github.com/btcsuite/btcd/chaincfg/params.go b/vendor/github.com/btcsuite/btcd/chaincfg/params.go index 54117b8..a6d8d3e 100644 --- a/vendor/github.com/btcsuite/btcd/chaincfg/params.go +++ b/vendor/github.com/btcsuite/btcd/chaincfg/params.go @@ -5,6 +5,8 @@ package chaincfg import ( + "encoding/binary" + "encoding/hex" "errors" "math" "math/big" @@ -38,6 +40,30 @@ var ( // simNetPowLimit is the highest proof of work value a Bitcoin block // can have for the simulation test network. It is the value 2^255 - 1. simNetPowLimit = new(big.Int).Sub(new(big.Int).Lsh(bigOne, 255), bigOne) + + // sigNetPowLimit is the highest proof of work value a bitcoin block can + // have for the signet test network. It is the value 0x0377ae << 216. + sigNetPowLimit = new(big.Int).Lsh(new(big.Int).SetInt64(0x0377ae), 216) + + // DefaultSignetChallenge is the byte representation of the signet + // challenge for the default (public, Taproot enabled) signet network. + // This is the binary equivalent of the bitcoin script + // 1 03ad5e0edad18cb1f0fc0d28a3d4f1f3e445640337489abb10404f2d1e086be430 + // 0359ef5021964fe22d6f8e05b2463c9540ce96883fe3b278760f048f5189f2e6c4 2 + // OP_CHECKMULTISIG + DefaultSignetChallenge, _ = hex.DecodeString( + "512103ad5e0edad18cb1f0fc0d28a3d4f1f3e445640337489abb10404f2d" + + "1e086be430210359ef5021964fe22d6f8e05b2463c9540ce9688" + + "3fe3b278760f048f5189f2e6c452ae", + ) + + // DefaultSignetDNSSeeds is the list of seed nodes for the default + // (public, Taproot enabled) signet network. + DefaultSignetDNSSeeds = []DNSSeed{ + {"178.128.221.177", false}, + {"2a01:7c8:d005:390::5", false}, + {"v7ajjeirttkbnt32wpy3c6w3emwnfr3fkla7hpxcfokr3ysd3kqtzmqd.onion:38333", false}, + } ) // Checkpoint identifies a known good point in the block chain. Using @@ -96,6 +122,11 @@ const ( // includes the deployment of BIPS 141, 142, 144, 145, 147 and 173. DeploymentSegwit + // DeploymentTaproot defines the rule change deployment ID for the + // Taproot (+Schnorr) soft-fork package. The taproot package includes + // the deployment of BIPS 340, 341 and 342. + DeploymentTaproot + // NOTE: DefinedDeployments must always come last since it is used to // determine how many defined deployments there currently are. @@ -578,6 +609,107 @@ var SimNetParams = Params{ HDCoinType: 115, // ASCII for s } +// SigNetParams defines the network parameters for the default public signet +// Bitcoin network. Not to be confused with the regression test network, this +// network is sometimes simply called "signet" or "taproot signet". +var SigNetParams = CustomSignetParams( + DefaultSignetChallenge, DefaultSignetDNSSeeds, +) + +// CustomSignetParams creates network parameters for a custom signet network +// from a challenge. The challenge is the binary compiled version of the block +// challenge script. +func CustomSignetParams(challenge []byte, dnsSeeds []DNSSeed) Params { + // The message start is defined as the first four bytes of the sha256d + // of the challenge script, as a single push (i.e. prefixed with the + // challenge script length). + challengeLength := byte(len(challenge)) + hashDouble := chainhash.DoubleHashB( + append([]byte{challengeLength}, challenge...), + ) + + // We use little endian encoding of the hash prefix to be in line with + // the other wire network identities. + net := binary.LittleEndian.Uint32(hashDouble[0:4]) + return Params{ + Name: "signet", + Net: wire.BitcoinNet(net), + DefaultPort: "38333", + DNSSeeds: dnsSeeds, + + // Chain parameters + GenesisBlock: &sigNetGenesisBlock, + GenesisHash: &sigNetGenesisHash, + PowLimit: sigNetPowLimit, + PowLimitBits: 0x1e0377ae, + BIP0034Height: 1, + BIP0065Height: 1, + BIP0066Height: 1, + CoinbaseMaturity: 100, + SubsidyReductionInterval: 210000, + TargetTimespan: time.Hour * 24 * 14, // 14 days + TargetTimePerBlock: time.Minute * 10, // 10 minutes + RetargetAdjustmentFactor: 4, // 25% less, 400% more + ReduceMinDifficulty: false, + MinDiffReductionTime: time.Minute * 20, // TargetTimePerBlock * 2 + GenerateSupported: false, + + // Checkpoints ordered from oldest to newest. + Checkpoints: nil, + + // Consensus rule change deployments. + // + // The miner confirmation window is defined as: + // target proof of work timespan / target proof of work spacing + RuleChangeActivationThreshold: 1916, // 95% of 2016 + MinerConfirmationWindow: 2016, + Deployments: [DefinedDeployments]ConsensusDeployment{ + DeploymentTestDummy: { + BitNumber: 28, + StartTime: 1199145601, // January 1, 2008 UTC + ExpireTime: 1230767999, // December 31, 2008 UTC + }, + DeploymentCSV: { + BitNumber: 29, + StartTime: 0, // Always available for vote + ExpireTime: math.MaxInt64, // Never expires + }, + DeploymentSegwit: { + BitNumber: 29, + StartTime: 0, // Always available for vote + ExpireTime: math.MaxInt64, // Never expires. + }, + DeploymentTaproot: { + BitNumber: 29, + StartTime: 0, // Always available for vote + ExpireTime: math.MaxInt64, // Never expires. + }, + }, + + // Mempool parameters + RelayNonStdTxs: false, + + // Human-readable part for Bech32 encoded segwit addresses, as defined in + // BIP 173. + Bech32HRPSegwit: "tb", // always tb for test net + + // Address encoding magics + PubKeyHashAddrID: 0x6f, // starts with m or n + ScriptHashAddrID: 0xc4, // starts with 2 + WitnessPubKeyHashAddrID: 0x03, // starts with QW + WitnessScriptHashAddrID: 0x28, // starts with T7n + PrivateKeyID: 0xef, // starts with 9 (uncompressed) or c (compressed) + + // BIP32 hierarchical deterministic extended key magics + HDPrivateKeyID: [4]byte{0x04, 0x35, 0x83, 0x94}, // starts with tprv + HDPublicKeyID: [4]byte{0x04, 0x35, 0x87, 0xcf}, // starts with tpub + + // BIP44 coin type used in the hierarchical deterministic path for + // address generation. + HDCoinType: 1, + } +} + var ( // ErrDuplicateNet describes an error where the parameters for a Bitcoin // network could not be set due to the network already being a standard @@ -588,6 +720,10 @@ var ( // is intended to identify the network for a hierarchical deterministic // private extended key is not registered. ErrUnknownHDKeyID = errors.New("unknown hd private extended key bytes") + + // ErrInvalidHDKeyID describes an error where the provided hierarchical + // deterministic version bytes, or hd key id, is malformed. + ErrInvalidHDKeyID = errors.New("invalid hd extended key version bytes") ) var ( @@ -619,7 +755,11 @@ func Register(params *Params) error { registeredNets[params.Net] = struct{}{} pubKeyHashAddrIDs[params.PubKeyHashAddrID] = struct{}{} scriptHashAddrIDs[params.ScriptHashAddrID] = struct{}{} - hdPrivToPubKeyIDs[params.HDPrivateKeyID] = params.HDPublicKeyID[:] + + err := RegisterHDKeyID(params.HDPublicKeyID[:], params.HDPrivateKeyID[:]) + if err != nil { + return err + } // A valid Bech32 encoded segwit address always has as prefix the // human-readable part for the given net followed by '1'. @@ -666,6 +806,29 @@ func IsBech32SegwitPrefix(prefix string) bool { return ok } +// RegisterHDKeyID registers a public and private hierarchical deterministic +// extended key ID pair. +// +// Non-standard HD version bytes, such as the ones documented in SLIP-0132, +// should be registered using this method for library packages to lookup key +// IDs (aka HD version bytes). When the provided key IDs are invalid, the +// ErrInvalidHDKeyID error will be returned. +// +// Reference: +// SLIP-0132 : Registered HD version bytes for BIP-0032 +// https://github.com/satoshilabs/slips/blob/master/slip-0132.md +func RegisterHDKeyID(hdPublicKeyID []byte, hdPrivateKeyID []byte) error { + if len(hdPublicKeyID) != 4 || len(hdPrivateKeyID) != 4 { + return ErrInvalidHDKeyID + } + + var keyID [4]byte + copy(keyID[:], hdPrivateKeyID) + hdPrivToPubKeyIDs[keyID] = hdPublicKeyID + + return nil +} + // HDPrivateKeyToPublicKeyID accepts a private hierarchical deterministic // extended key id and returns the associated public key id. When the provided // id is not registered, the ErrUnknownHDKeyID error will be returned. diff --git a/vendor/github.com/btcsuite/btcd/wire/README.md b/vendor/github.com/btcsuite/btcd/wire/README.md index 646bc0a..8660bbf 100644 --- a/vendor/github.com/btcsuite/btcd/wire/README.md +++ b/vendor/github.com/btcsuite/btcd/wire/README.md @@ -1,9 +1,9 @@ wire ==== -[![Build Status](http://img.shields.io/travis/btcsuite/btcd.svg)](https://travis-ci.org/btcsuite/btcd) +[![Build Status](https://github.com/btcsuite/btcd/workflows/Build%20and%20Test/badge.svg)](https://github.com/btcsuite/btcd/actions) [![ISC License](http://img.shields.io/badge/license-ISC-blue.svg)](http://copyfree.org) -[![GoDoc](https://img.shields.io/badge/godoc-reference-blue.svg)](http://godoc.org/github.com/btcsuite/btcd/wire) +[![GoDoc](https://img.shields.io/badge/godoc-reference-blue.svg)](https://pkg.go.dev/github.com/btcsuite/btcd/wire) ======= Package wire implements the bitcoin wire protocol. A comprehensive suite of diff --git a/vendor/github.com/btcsuite/btcd/wire/message.go b/vendor/github.com/btcsuite/btcd/wire/message.go index e937647..6d3147a 100644 --- a/vendor/github.com/btcsuite/btcd/wire/message.go +++ b/vendor/github.com/btcsuite/btcd/wire/message.go @@ -57,6 +57,7 @@ const ( CmdCFilter = "cfilter" CmdCFHeaders = "cfheaders" CmdCFCheckpt = "cfcheckpt" + CmdSendAddrV2 = "sendaddrv2" ) // MessageEncoding represents the wire message encoding format to be used. @@ -99,6 +100,9 @@ func makeEmptyMessage(command string) (Message, error) { case CmdVerAck: msg = &MsgVerAck{} + case CmdSendAddrV2: + msg = &MsgSendAddrV2{} + case CmdGetAddr: msg = &MsgGetAddr{} @@ -213,7 +217,7 @@ func readMessageHeader(r io.Reader) (int, *messageHeader, error) { readElements(hr, &hdr.magic, &command, &hdr.length, &hdr.checksum) // Strip trailing zeros from command string. - hdr.command = string(bytes.TrimRight(command[:], string(0))) + hdr.command = string(bytes.TrimRight(command[:], "\x00")) return n, &hdr, nil } @@ -401,7 +405,7 @@ func ReadMessageWithEncodingN(r io.Reader, pver uint32, btcnet BitcoinNet, // Test checksum. checksum := chainhash.DoubleHashB(payload)[0:4] - if !bytes.Equal(checksum[:], hdr.checksum[:]) { + if !bytes.Equal(checksum, hdr.checksum[:]) { str := fmt.Sprintf("payload checksum failed - header "+ "indicates %v, but actual checksum is %v.", hdr.checksum, checksum) diff --git a/vendor/github.com/btcsuite/btcd/wire/msgsendaddrv2.go b/vendor/github.com/btcsuite/btcd/wire/msgsendaddrv2.go new file mode 100644 index 0000000..d6d19ef --- /dev/null +++ b/vendor/github.com/btcsuite/btcd/wire/msgsendaddrv2.go @@ -0,0 +1,42 @@ +package wire + +import ( + "io" +) + +// MsgSendAddrV2 defines a bitcoin sendaddrv2 message which is used for a peer +// to signal support for receiving ADDRV2 messages (BIP155). It implements the +// Message interface. +// +// This message has no payload. +type MsgSendAddrV2 struct{} + +// BtcDecode decodes r using the bitcoin protocol encoding into the receiver. +// This is part of the Message interface implementation. +func (msg *MsgSendAddrV2) BtcDecode(r io.Reader, pver uint32, enc MessageEncoding) error { + return nil +} + +// BtcEncode encodes the receiver to w using the bitcoin protocol encoding. +// This is part of the Message interface implementation. +func (msg *MsgSendAddrV2) BtcEncode(w io.Writer, pver uint32, enc MessageEncoding) error { + return nil +} + +// Command returns the protocol command string for the message. This is part +// of the Message interface implementation. +func (msg *MsgSendAddrV2) Command() string { + return CmdSendAddrV2 +} + +// MaxPayloadLength returns the maximum length the payload can be for the +// receiver. This is part of the Message interface implementation. +func (msg *MsgSendAddrV2) MaxPayloadLength(pver uint32) uint32 { + return 0 +} + +// NewMsgSendAddrV2 returns a new bitcoin sendaddrv2 message that conforms to the +// Message interface. +func NewMsgSendAddrV2() *MsgSendAddrV2 { + return &MsgSendAddrV2{} +} diff --git a/vendor/github.com/btcsuite/btcd/wire/msgtx.go b/vendor/github.com/btcsuite/btcd/wire/msgtx.go index 4f10ef9..1e2f69f 100644 --- a/vendor/github.com/btcsuite/btcd/wire/msgtx.go +++ b/vendor/github.com/btcsuite/btcd/wire/msgtx.go @@ -109,14 +109,38 @@ const ( maxWitnessItemSize = 11000 ) -// witnessMarkerBytes are a pair of bytes specific to the witness encoding. If -// this sequence is encoutered, then it indicates a transaction has iwtness -// data. The first byte is an always 0x00 marker byte, which allows decoders to -// distinguish a serialized transaction with witnesses from a regular (legacy) -// one. The second byte is the Flag field, which at the moment is always 0x01, -// but may be extended in the future to accommodate auxiliary non-committed -// fields. -var witessMarkerBytes = []byte{0x00, 0x01} +// TxFlagMarker is the first byte of the FLAG field in a bitcoin tx +// message. It allows decoders to distinguish a regular serialized +// transaction from one that would require a different parsing logic. +// +// Position of FLAG in a bitcoin tx message: +// ┌─────────┬────────────────────┬─────────────┬─────┐ +// │ VERSION │ FLAG │ TX-IN-COUNT │ ... │ +// │ 4 bytes │ 2 bytes (optional) │ varint │ │ +// └─────────┴────────────────────┴─────────────┴─────┘ +// +// Zooming into the FLAG field: +// ┌── FLAG ─────────────┬────────┐ +// │ TxFlagMarker (0x00) │ TxFlag │ +// │ 1 byte │ 1 byte │ +// └─────────────────────┴────────┘ +const TxFlagMarker = 0x00 + +// TxFlag is the second byte of the FLAG field in a bitcoin tx message. +// It indicates the decoding logic to use in the transaction parser, if +// TxFlagMarker is detected in the tx message. +// +// As of writing this, only the witness flag (0x01) is supported, but may be +// extended in the future to accommodate auxiliary non-committed fields. +type TxFlag = byte + +const ( + // WitnessFlag is a flag specific to witness encoding. If the TxFlagMarker + // is encountered followed by the WitnessFlag, then it indicates a + // transaction has witness data. This allows decoders to distinguish a + // serialized transaction with witnesses from a legacy one. + WitnessFlag TxFlag = 0x01 +) // scriptFreeList defines a free list of byte slices (up to the maximum number // defined by the freeListMaxItems constant) that have a cap according to the @@ -420,18 +444,19 @@ func (msg *MsgTx) BtcDecode(r io.Reader, pver uint32, enc MessageEncoding) error return err } - // A count of zero (meaning no TxIn's to the uninitiated) indicates - // this is a transaction with witness data. - var flag [1]byte - if count == 0 && enc == WitnessEncoding { - // Next, we need to read the flag, which is a single byte. + // A count of zero (meaning no TxIn's to the uninitiated) means that the + // value is a TxFlagMarker, and hence indicates the presence of a flag. + var flag [1]TxFlag + if count == TxFlagMarker && enc == WitnessEncoding { + // The count varint was in fact the flag marker byte. Next, we need to + // read the flag value, which is a single byte. if _, err = io.ReadFull(r, flag[:]); err != nil { return err } - // At the moment, the flag MUST be 0x01. In the future other - // flag types may be supported. - if flag[0] != 0x01 { + // At the moment, the flag MUST be WitnessFlag (0x01). In the future + // other flag types may be supported. + if flag[0] != WitnessFlag { str := fmt.Sprintf("witness tx but flag byte is %x", flag) return messageError("MsgTx.BtcDecode", str) } @@ -690,14 +715,11 @@ func (msg *MsgTx) BtcEncode(w io.Writer, pver uint32, enc MessageEncoding) error // defined in BIP0144. doWitness := enc == WitnessEncoding && msg.HasWitness() if doWitness { - // After the txn's Version field, we include two additional - // bytes specific to the witness encoding. The first byte is an - // always 0x00 marker byte, which allows decoders to - // distinguish a serialized transaction with witnesses from a - // regular (legacy) one. The second byte is the Flag field, - // which at the moment is always 0x01, but may be extended in - // the future to accommodate auxiliary non-committed fields. - if _, err := w.Write(witessMarkerBytes); err != nil { + // After the transaction's Version field, we include two additional + // bytes specific to the witness encoding. This byte sequence is known + // as a flag. The first byte is a marker byte (TxFlagMarker) and the + // second one is the flag value to indicate presence of witness data. + if _, err := w.Write([]byte{TxFlagMarker, WitnessFlag}); err != nil { return err } } diff --git a/vendor/github.com/btcsuite/btcutil/.gitignore b/vendor/github.com/btcsuite/btcutil/.gitignore index 5b97dbb..21c8043 100644 --- a/vendor/github.com/btcsuite/btcutil/.gitignore +++ b/vendor/github.com/btcsuite/btcutil/.gitignore @@ -1,28 +1,28 @@ -# Temp files -*~ - -# Log files -*.log - -# Compiled Object files, Static and Dynamic libs (Shared Objects) -*.o -*.a -*.so - -# Folders -_obj -_test - -# Architecture specific extensions/prefixes -*.[568vq] -[568vq].out - -*.cgo1.go -*.cgo2.c -_cgo_defun.c -_cgo_gotypes.go -_cgo_export.* - -_testmain.go - -*.exe +# Temp files +*~ + +# Log files +*.log + +# Compiled Object files, Static and Dynamic libs (Shared Objects) +*.o +*.a +*.so + +# Folders +_obj +_test + +# Architecture specific extensions/prefixes +*.[568vq] +[568vq].out + +*.cgo1.go +*.cgo2.c +_cgo_defun.c +_cgo_gotypes.go +_cgo_export.* + +_testmain.go + +*.exe diff --git a/vendor/github.com/btcsuite/btcutil/go.mod b/vendor/github.com/btcsuite/btcutil/go.mod deleted file mode 100644 index 11583b4..0000000 --- a/vendor/github.com/btcsuite/btcutil/go.mod +++ /dev/null @@ -1,11 +0,0 @@ -module github.com/btcsuite/btcutil - -go 1.14 - -require ( - github.com/aead/siphash v1.0.1 - github.com/btcsuite/btcd v0.20.1-beta - github.com/davecgh/go-spew v1.1.0 - github.com/kkdai/bstream v0.0.0-20161212061736-f391b8402d23 - golang.org/x/crypto v0.0.0-20200115085410-6d4e4cb37c7d -) diff --git a/vendor/github.com/btcsuite/btcutil/go.sum b/vendor/github.com/btcsuite/btcutil/go.sum deleted file mode 100644 index 2678e65..0000000 --- a/vendor/github.com/btcsuite/btcutil/go.sum +++ /dev/null @@ -1,59 +0,0 @@ -github.com/aead/siphash v1.0.1 h1:FwHfE/T45KPKYuuSAKyyvE+oPWcaQ+CUmFW0bPlM+kg= -github.com/aead/siphash v1.0.1/go.mod h1:Nywa3cDsYNNK3gaciGTWPwHt0wlpNV15vwmswBAUSII= -github.com/btcsuite/btcd v0.20.1-beta h1:Ik4hyJqN8Jfyv3S4AGBOmyouMsYE3EdYODkMbQjwPGw= -github.com/btcsuite/btcd v0.20.1-beta/go.mod h1:wVuoA8VJLEcwgqHBwHmzLRazpKxTv13Px/pDuV7OomQ= -github.com/btcsuite/btclog v0.0.0-20170628155309-84c8d2346e9f h1:bAs4lUbRJpnnkd9VhRV3jjAVU7DJVjMaK+IsvSeZvFo= -github.com/btcsuite/btclog v0.0.0-20170628155309-84c8d2346e9f/go.mod h1:TdznJufoqS23FtqVCzL0ZqgP5MqXbb4fg/WgDys70nA= -github.com/btcsuite/btcutil v0.0.0-20190425235716-9e5f4b9a998d/go.mod h1:+5NJ2+qvTyV9exUAL/rxXi3DcLg2Ts+ymUAY5y4NvMg= -github.com/btcsuite/go-socks v0.0.0-20170105172521-4720035b7bfd/go.mod h1:HHNXQzUsZCxOoE+CPiyCTO6x34Zs86zZUiwtpXoGdtg= -github.com/btcsuite/goleveldb v0.0.0-20160330041536-7834afc9e8cd h1:qdGvebPBDuYDPGi1WCPjy1tGyMpmDK8IEapSsszn7HE= -github.com/btcsuite/goleveldb v0.0.0-20160330041536-7834afc9e8cd/go.mod h1:F+uVaaLLH7j4eDXPRvw78tMflu7Ie2bzYOH4Y8rRKBY= -github.com/btcsuite/snappy-go v0.0.0-20151229074030-0bdef8d06723 h1:ZA/jbKoGcVAnER6pCHPEkGdZOV7U1oLUedErBHCUMs0= -github.com/btcsuite/snappy-go v0.0.0-20151229074030-0bdef8d06723/go.mod h1:8woku9dyThutzjeg+3xrA5iCpBRH8XEEg3lh6TiUghc= -github.com/btcsuite/websocket v0.0.0-20150119174127-31079b680792/go.mod h1:ghJtEyQwv5/p4Mg4C0fgbePVuGr935/5ddU9Z3TmDRY= -github.com/btcsuite/winsvc v1.0.0/go.mod h1:jsenWakMcC0zFBFurPLEAyrnc/teJEM1O46fmI40EZs= -github.com/davecgh/go-spew v0.0.0-20171005155431-ecdeabc65495 h1:6IyqGr3fnd0tM3YxipK27TUskaOVUjU2nG45yzwcQKY= -github.com/davecgh/go-spew v0.0.0-20171005155431-ecdeabc65495/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38= -github.com/davecgh/go-spew v1.1.0 h1:ZDRjVQ15GmhC3fiQ8ni8+OwkZQO4DARzQgrnXU1Liz8= -github.com/davecgh/go-spew v1.1.0/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38= -github.com/fsnotify/fsnotify v1.4.7 h1:IXs+QLmnXW2CcXuY+8Mzv/fWEsPGWxqefPtCP5CnV9I= -github.com/fsnotify/fsnotify v1.4.7/go.mod h1:jwhsz4b93w/PPRr/qN1Yymfu8t87LnFCMoQvtojpjFo= -github.com/golang/protobuf v1.2.0 h1:P3YflyNX/ehuJFLhxviNdFxQPkGK5cDcApsge1SqnvM= -github.com/golang/protobuf v1.2.0/go.mod h1:6lQm79b+lXiMfvg/cZm0SGofjICqVBUtrP5yJMmIC1U= -github.com/hpcloud/tail v1.0.0 h1:nfCOvKYfkgYP8hkirhJocXT2+zOD8yUNjXaWfTlyFKI= -github.com/hpcloud/tail v1.0.0/go.mod h1:ab1qPbhIpdTxEkNHXyeSf5vhxWSCs/tWer42PpOxQnU= -github.com/jessevdk/go-flags v0.0.0-20141203071132-1679536dcc89/go.mod h1:4FA24M0QyGHXBuZZK/XkWh8h0e1EYbRYJSGM75WSRxI= -github.com/jrick/logrotate v1.0.0/go.mod h1:LNinyqDIJnpAur+b8yyulnQw/wDuN1+BYKlTRt3OuAQ= -github.com/kkdai/bstream v0.0.0-20161212061736-f391b8402d23 h1:FOOIBWrEkLgmlgGfMuZT83xIwfPDxEI2OHu6xUmJMFE= -github.com/kkdai/bstream v0.0.0-20161212061736-f391b8402d23/go.mod h1:J+Gs4SYgM6CZQHDETBtE9HaSEkGmuNXF86RwHhHUvq4= -github.com/onsi/ginkgo v1.6.0/go.mod h1:lLunBs/Ym6LB5Z9jYTR76FiuTmxDTDusOGeTQH+WWjE= -github.com/onsi/ginkgo v1.7.0 h1:WSHQ+IS43OoUrWtD1/bbclrwK8TTH5hzp+umCiuxHgs= -github.com/onsi/ginkgo v1.7.0/go.mod h1:lLunBs/Ym6LB5Z9jYTR76FiuTmxDTDusOGeTQH+WWjE= -github.com/onsi/gomega v1.4.3 h1:RE1xgDvH7imwFD45h+u2SgIfERHlS2yNG4DObb5BSKU= -github.com/onsi/gomega v1.4.3/go.mod h1:ex+gbHU/CVuBBDIJjb2X0qEXbFg53c61hWP/1CpauHY= -golang.org/x/crypto v0.0.0-20170930174604-9419663f5a44 h1:9lP3x0pW80sDI6t1UMSLA4to18W7R7imwAI/sWS9S8Q= -golang.org/x/crypto v0.0.0-20170930174604-9419663f5a44/go.mod h1:6SG95UA2DQfeDnfUPMdvaQW0Q7yPrPDi9nlGo2tz2b4= -golang.org/x/crypto v0.0.0-20190308221718-c2843e01d9a2/go.mod h1:djNgcEr1/C05ACkg1iLfiJU5Ep61QUkGW8qpdssI0+w= -golang.org/x/crypto v0.0.0-20200115085410-6d4e4cb37c7d h1:2+ZP7EfsZV7Vvmx3TIqSlSzATMkTAKqM14YGFPoSKjI= -golang.org/x/crypto v0.0.0-20200115085410-6d4e4cb37c7d/go.mod h1:LzIPMQfyMNhhGPhUkYOs5KpL4U8rLKemX1yGLhDgUto= -golang.org/x/net v0.0.0-20180906233101-161cd47e91fd h1:nTDtHvHSdCn1m6ITfMRqtOd/9+7a3s8RBNOZ3eYZzJA= -golang.org/x/net v0.0.0-20180906233101-161cd47e91fd/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4= -golang.org/x/net v0.0.0-20190404232315-eb5bcb51f2a3 h1:0GoQqolDA55aaLxZyTzK/Y2ePZzZTUrRacwib7cNsYQ= -golang.org/x/net v0.0.0-20190404232315-eb5bcb51f2a3/go.mod h1:t9HGtf8HONx5eT2rtn7q6eTqICYqUVnKs3thJo3Qplg= -golang.org/x/sync v0.0.0-20180314180146-1d60e4601c6f h1:wMNYb4v58l5UBM7MYRLPG6ZhfOqbKu7X5eyFl8ZhKvA= -golang.org/x/sync v0.0.0-20180314180146-1d60e4601c6f/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM= -golang.org/x/sys v0.0.0-20180909124046-d0be0721c37e h1:o3PsSEY8E4eXWkXrIP9YJALUkVZqzHJT5DOasTyn8Vs= -golang.org/x/sys v0.0.0-20180909124046-d0be0721c37e/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY= -golang.org/x/sys v0.0.0-20190215142949-d0b11bdaac8a/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY= -golang.org/x/sys v0.0.0-20190412213103-97732733099d h1:+R4KGOnez64A81RvjARKc4UT5/tI9ujCIVX+P5KiHuI= -golang.org/x/sys v0.0.0-20190412213103-97732733099d/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs= -golang.org/x/text v0.3.0 h1:g61tztE5qeGQ89tm6NTjjM9VPIm088od1l6aSorWRWg= -golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ= -gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405 h1:yhCVgyC4o1eVCa2tZl7eS0r+SDo693bJlVdllGtEeKM= -gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0= -gopkg.in/fsnotify.v1 v1.4.7 h1:xOHLXZwVvI9hhs+cLKq5+I5onOuwQLhQwiu63xxlHs4= -gopkg.in/fsnotify.v1 v1.4.7/go.mod h1:Tz8NjZHkW78fSQdbUxIjBTcgA1z1m8ZHf0WmKUhAMys= -gopkg.in/tomb.v1 v1.0.0-20141024135613-dd632973f1e7 h1:uRGJdciOHaEIrze2W8Q3AKkepLTh2hOroT7a+7czfdQ= -gopkg.in/tomb.v1 v1.0.0-20141024135613-dd632973f1e7/go.mod h1:dt/ZhP58zS4L8KSrWDmTeBkI65Dw0HsyUHuEVlX15mw= -gopkg.in/yaml.v2 v2.2.1 h1:mUhvW9EsL+naU5Q3cakzfE91YhliOondGd6ZrsDBHQE= -gopkg.in/yaml.v2 v2.2.1/go.mod h1:hI93XBmqTisBFMUTm0b8Fm+jr3Dg1NNxqwp+5A1VGuI= diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/LICENSE b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/LICENSE new file mode 100644 index 0000000..d2d1dd9 --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/LICENSE @@ -0,0 +1,17 @@ +ISC License + +Copyright (c) 2013-2017 The btcsuite developers +Copyright (c) 2015-2020 The Decred developers +Copyright (c) 2017 The Lightning Network Developers + +Permission to use, copy, modify, and distribute this software for any +purpose with or without fee is hereby granted, provided that the above +copyright notice and this permission notice appear in all copies. + +THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES +WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF +MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR +ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES +WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN +ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF +OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/README.md b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/README.md new file mode 100644 index 0000000..b84bcdb --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/README.md @@ -0,0 +1,72 @@ +secp256k1 +========= + +[![Build Status](https://github.com/decred/dcrd/workflows/Build%20and%20Test/badge.svg)](https://github.com/decred/dcrd/actions) +[![ISC License](https://img.shields.io/badge/license-ISC-blue.svg)](http://copyfree.org) +[![Doc](https://img.shields.io/badge/doc-reference-blue.svg)](https://pkg.go.dev/github.com/decred/dcrd/dcrec/secp256k1/v4) + +Package secp256k1 implements optimized secp256k1 elliptic curve operations. + +This package provides an optimized pure Go implementation of elliptic curve +cryptography operations over the secp256k1 curve as well as data structures and +functions for working with public and private secp256k1 keys. See +https://www.secg.org/sec2-v2.pdf for details on the standard. + +In addition, sub packages are provided to produce, verify, parse, and serialize +ECDSA signatures and EC-Schnorr-DCRv0 (a custom Schnorr-based signature scheme +specific to Decred) signatures. See the README.md files in the relevant sub +packages for more details about those aspects. + +An overview of the features provided by this package are as follows: + +- Private key generation, serialization, and parsing +- Public key generation, serialization and parsing per ANSI X9.62-1998 + - Parses uncompressed, compressed, and hybrid public keys + - Serializes uncompressed and compressed public keys +- Specialized types for performing optimized and constant time field operations + - `FieldVal` type for working modulo the secp256k1 field prime + - `ModNScalar` type for working modulo the secp256k1 group order +- Elliptic curve operations in Jacobian projective coordinates + - Point addition + - Point doubling + - Scalar multiplication with an arbitrary point + - Scalar multiplication with the base point (group generator) +- Point decompression from a given x coordinate +- Nonce generation via RFC6979 with support for extra data and version + information that can be used to prevent nonce reuse between signing algorithms + +It also provides an implementation of the Go standard library `crypto/elliptic` +`Curve` interface via the `S256` function so that it may be used with other +packages in the standard library such as `crypto/tls`, `crypto/x509`, and +`crypto/ecdsa`. However, in the case of ECDSA, it is highly recommended to use +the `ecdsa` sub package of this package instead since it is optimized +specifically for secp256k1 and is significantly faster as a result. + +Although this package was primarily written for dcrd, it has intentionally been +designed so it can be used as a standalone package for any projects needing to +use optimized secp256k1 elliptic curve cryptography. + +Finally, a comprehensive suite of tests is provided to provide a high level of +quality assurance. + +## secp256k1 use in Decred + +At the time of this writing, the primary public key cryptography in widespread +use on the Decred network used to secure coins is based on elliptic curves +defined by the secp256k1 domain parameters. + +## Installation and Updating + +This package is part of the `github.com/decred/dcrd/dcrec/secp256k1/v4` module. +Use the standard go tooling for working with modules to incorporate it. + +## Examples + +* [Encryption](https://pkg.go.dev/github.com/decred/dcrd/dcrec/secp256k1/v4#example-package-EncryptDecryptMessage) + Demonstrates encrypting and decrypting a message using a shared key derived + through ECDHE. + +## License + +Package secp256k1 is licensed under the [copyfree](http://copyfree.org) ISC +License. diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/compressedbytepoints.go b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/compressedbytepoints.go new file mode 100644 index 0000000..ca4be12 --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/compressedbytepoints.go @@ -0,0 +1,11 @@ +// Copyright (c) 2015 The btcsuite developers +// Copyright (c) 2015-2021 The Decred developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +package secp256k1 + +// Auto-generated file (see genprecomps.go) +// DO NOT EDIT + +var compressedBytePoints = 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" diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/curve.go b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/curve.go new file mode 100644 index 0000000..e862060 --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/curve.go @@ -0,0 +1,943 @@ +// Copyright (c) 2015-2021 The Decred developers +// Copyright 2013-2014 The btcsuite developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +package secp256k1 + +import ( + "encoding/hex" + "math/big" +) + +// References: +// [SECG]: Recommended Elliptic Curve Domain Parameters +// https://www.secg.org/sec2-v2.pdf +// +// [GECC]: Guide to Elliptic Curve Cryptography (Hankerson, Menezes, Vanstone) +// +// [BRID]: On Binary Representations of Integers with Digits -1, 0, 1 +// (Prodinger, Helmut) + +// All group operations are performed using Jacobian coordinates. For a given +// (x, y) position on the curve, the Jacobian coordinates are (x1, y1, z1) +// where x = x1/z1^2 and y = y1/z1^3. + +// hexToFieldVal converts the passed hex string into a FieldVal and will panic +// if there is an error. This is only provided for the hard-coded constants so +// errors in the source code can be detected. It will only (and must only) be +// called with hard-coded values. +func hexToFieldVal(s string) *FieldVal { + b, err := hex.DecodeString(s) + if err != nil { + panic("invalid hex in source file: " + s) + } + var f FieldVal + if overflow := f.SetByteSlice(b); overflow { + panic("hex in source file overflows mod P: " + s) + } + return &f +} + +var ( + // Next 6 constants are from Hal Finney's bitcointalk.org post: + // https://bitcointalk.org/index.php?topic=3238.msg45565#msg45565 + // May he rest in peace. + // + // They have also been independently derived from the code in the + // EndomorphismVectors function in genstatics.go. + endomorphismLambda = fromHex("5363ad4cc05c30e0a5261c028812645a122e22ea20816678df02967c1b23bd72") + endomorphismBeta = hexToFieldVal("7ae96a2b657c07106e64479eac3434e99cf0497512f58995c1396c28719501ee") + endomorphismA1 = fromHex("3086d221a7d46bcde86c90e49284eb15") + endomorphismB1 = fromHex("-e4437ed6010e88286f547fa90abfe4c3") + endomorphismA2 = fromHex("114ca50f7a8e2f3f657c1108d9d44cfd8") + endomorphismB2 = fromHex("3086d221a7d46bcde86c90e49284eb15") + + // Alternatively, the following parameters are valid as well, however, they + // seem to be about 8% slower in practice. + // + // endomorphismLambda = fromHex("AC9C52B33FA3CF1F5AD9E3FD77ED9BA4A880B9FC8EC739C2E0CFC810B51283CE") + // endomorphismBeta = hexToFieldVal("851695D49A83F8EF919BB86153CBCB16630FB68AED0A766A3EC693D68E6AFA40") + // endomorphismA1 = fromHex("E4437ED6010E88286F547FA90ABFE4C3") + // endomorphismB1 = fromHex("-3086D221A7D46BCDE86C90E49284EB15") + // endomorphismA2 = fromHex("3086D221A7D46BCDE86C90E49284EB15") + // endomorphismB2 = fromHex("114CA50F7A8E2F3F657C1108D9D44CFD8") +) + +// JacobianPoint is an element of the group formed by the secp256k1 curve in +// Jacobian projective coordinates and thus represents a point on the curve. +type JacobianPoint struct { + // The X coordinate in Jacobian projective coordinates. The affine point is + // X/z^2. + X FieldVal + + // The Y coordinate in Jacobian projective coordinates. The affine point is + // Y/z^3. + Y FieldVal + + // The Z coordinate in Jacobian projective coordinates. + Z FieldVal +} + +// MakeJacobianPoint returns a Jacobian point with the provided X, Y, and Z +// coordinates. +func MakeJacobianPoint(x, y, z *FieldVal) JacobianPoint { + var p JacobianPoint + p.X.Set(x) + p.Y.Set(y) + p.Z.Set(z) + return p +} + +// Set sets the Jacobian point to the provided point. +func (p *JacobianPoint) Set(other *JacobianPoint) { + p.X.Set(&other.X) + p.Y.Set(&other.Y) + p.Z.Set(&other.Z) +} + +// ToAffine reduces the Z value of the existing point to 1 effectively +// making it an affine coordinate in constant time. The point will be +// normalized. +func (p *JacobianPoint) ToAffine() { + // Inversions are expensive and both point addition and point doubling + // are faster when working with points that have a z value of one. So, + // if the point needs to be converted to affine, go ahead and normalize + // the point itself at the same time as the calculation is the same. + var zInv, tempZ FieldVal + zInv.Set(&p.Z).Inverse() // zInv = Z^-1 + tempZ.SquareVal(&zInv) // tempZ = Z^-2 + p.X.Mul(&tempZ) // X = X/Z^2 (mag: 1) + p.Y.Mul(tempZ.Mul(&zInv)) // Y = Y/Z^3 (mag: 1) + p.Z.SetInt(1) // Z = 1 (mag: 1) + + // Normalize the x and y values. + p.X.Normalize() + p.Y.Normalize() +} + +// addZ1AndZ2EqualsOne adds two Jacobian points that are already known to have +// z values of 1 and stores the result in the provided result param. That is to +// say result = p1 + p2. It performs faster addition than the generic add +// routine since less arithmetic is needed due to the ability to avoid the z +// value multiplications. +// +// NOTE: The points must be normalized for this function to return the correct +// result. The resulting point will be normalized. +func addZ1AndZ2EqualsOne(p1, p2, result *JacobianPoint) { + // To compute the point addition efficiently, this implementation splits + // the equation into intermediate elements which are used to minimize + // the number of field multiplications using the method shown at: + // https://hyperelliptic.org/EFD/g1p/auto-shortw-jacobian-0.html#addition-mmadd-2007-bl + // + // In particular it performs the calculations using the following: + // H = X2-X1, HH = H^2, I = 4*HH, J = H*I, r = 2*(Y2-Y1), V = X1*I + // X3 = r^2-J-2*V, Y3 = r*(V-X3)-2*Y1*J, Z3 = 2*H + // + // This results in a cost of 4 field multiplications, 2 field squarings, + // 6 field additions, and 5 integer multiplications. + x1, y1 := &p1.X, &p1.Y + x2, y2 := &p2.X, &p2.Y + x3, y3, z3 := &result.X, &result.Y, &result.Z + + // When the x coordinates are the same for two points on the curve, the + // y coordinates either must be the same, in which case it is point + // doubling, or they are opposite and the result is the point at + // infinity per the group law for elliptic curve cryptography. + if x1.Equals(x2) { + if y1.Equals(y2) { + // Since x1 == x2 and y1 == y2, point doubling must be + // done, otherwise the addition would end up dividing + // by zero. + DoubleNonConst(p1, result) + return + } + + // Since x1 == x2 and y1 == -y2, the sum is the point at + // infinity per the group law. + x3.SetInt(0) + y3.SetInt(0) + z3.SetInt(0) + return + } + + // Calculate X3, Y3, and Z3 according to the intermediate elements + // breakdown above. + var h, i, j, r, v FieldVal + var negJ, neg2V, negX3 FieldVal + h.Set(x1).Negate(1).Add(x2) // H = X2-X1 (mag: 3) + i.SquareVal(&h).MulInt(4) // I = 4*H^2 (mag: 4) + j.Mul2(&h, &i) // J = H*I (mag: 1) + r.Set(y1).Negate(1).Add(y2).MulInt(2) // r = 2*(Y2-Y1) (mag: 6) + v.Mul2(x1, &i) // V = X1*I (mag: 1) + negJ.Set(&j).Negate(1) // negJ = -J (mag: 2) + neg2V.Set(&v).MulInt(2).Negate(2) // neg2V = -(2*V) (mag: 3) + x3.Set(&r).Square().Add(&negJ).Add(&neg2V) // X3 = r^2-J-2*V (mag: 6) + negX3.Set(x3).Negate(6) // negX3 = -X3 (mag: 7) + j.Mul(y1).MulInt(2).Negate(2) // J = -(2*Y1*J) (mag: 3) + y3.Set(&v).Add(&negX3).Mul(&r).Add(&j) // Y3 = r*(V-X3)-2*Y1*J (mag: 4) + z3.Set(&h).MulInt(2) // Z3 = 2*H (mag: 6) + + // Normalize the resulting field values to a magnitude of 1 as needed. + x3.Normalize() + y3.Normalize() + z3.Normalize() +} + +// addZ1EqualsZ2 adds two Jacobian points that are already known to have the +// same z value and stores the result in the provided result param. That is to +// say result = p1 + p2. It performs faster addition than the generic add +// routine since less arithmetic is needed due to the known equivalence. +// +// NOTE: The points must be normalized for this function to return the correct +// result. The resulting point will be normalized. +func addZ1EqualsZ2(p1, p2, result *JacobianPoint) { + // To compute the point addition efficiently, this implementation splits + // the equation into intermediate elements which are used to minimize + // the number of field multiplications using a slightly modified version + // of the method shown at: + // https://hyperelliptic.org/EFD/g1p/auto-shortw-jacobian-0.html#addition-mmadd-2007-bl + // + // In particular it performs the calculations using the following: + // A = X2-X1, B = A^2, C=Y2-Y1, D = C^2, E = X1*B, F = X2*B + // X3 = D-E-F, Y3 = C*(E-X3)-Y1*(F-E), Z3 = Z1*A + // + // This results in a cost of 5 field multiplications, 2 field squarings, + // 9 field additions, and 0 integer multiplications. + x1, y1, z1 := &p1.X, &p1.Y, &p1.Z + x2, y2 := &p2.X, &p2.Y + x3, y3, z3 := &result.X, &result.Y, &result.Z + + // When the x coordinates are the same for two points on the curve, the + // y coordinates either must be the same, in which case it is point + // doubling, or they are opposite and the result is the point at + // infinity per the group law for elliptic curve cryptography. + if x1.Equals(x2) { + if y1.Equals(y2) { + // Since x1 == x2 and y1 == y2, point doubling must be + // done, otherwise the addition would end up dividing + // by zero. + DoubleNonConst(p1, result) + return + } + + // Since x1 == x2 and y1 == -y2, the sum is the point at + // infinity per the group law. + x3.SetInt(0) + y3.SetInt(0) + z3.SetInt(0) + return + } + + // Calculate X3, Y3, and Z3 according to the intermediate elements + // breakdown above. + var a, b, c, d, e, f FieldVal + var negX1, negY1, negE, negX3 FieldVal + negX1.Set(x1).Negate(1) // negX1 = -X1 (mag: 2) + negY1.Set(y1).Negate(1) // negY1 = -Y1 (mag: 2) + a.Set(&negX1).Add(x2) // A = X2-X1 (mag: 3) + b.SquareVal(&a) // B = A^2 (mag: 1) + c.Set(&negY1).Add(y2) // C = Y2-Y1 (mag: 3) + d.SquareVal(&c) // D = C^2 (mag: 1) + e.Mul2(x1, &b) // E = X1*B (mag: 1) + negE.Set(&e).Negate(1) // negE = -E (mag: 2) + f.Mul2(x2, &b) // F = X2*B (mag: 1) + x3.Add2(&e, &f).Negate(3).Add(&d) // X3 = D-E-F (mag: 5) + negX3.Set(x3).Negate(5).Normalize() // negX3 = -X3 (mag: 1) + y3.Set(y1).Mul(f.Add(&negE)).Negate(3) // Y3 = -(Y1*(F-E)) (mag: 4) + y3.Add(e.Add(&negX3).Mul(&c)) // Y3 = C*(E-X3)+Y3 (mag: 5) + z3.Mul2(z1, &a) // Z3 = Z1*A (mag: 1) + + // Normalize the resulting field values to a magnitude of 1 as needed. + x3.Normalize() + y3.Normalize() + z3.Normalize() +} + +// addZ2EqualsOne adds two Jacobian points when the second point is already +// known to have a z value of 1 (and the z value for the first point is not 1) +// and stores the result in the provided result param. That is to say result = +// p1 + p2. It performs faster addition than the generic add routine since +// less arithmetic is needed due to the ability to avoid multiplications by the +// second point's z value. +// +// NOTE: The points must be normalized for this function to return the correct +// result. The resulting point will be normalized. +func addZ2EqualsOne(p1, p2, result *JacobianPoint) { + // To compute the point addition efficiently, this implementation splits + // the equation into intermediate elements which are used to minimize + // the number of field multiplications using the method shown at: + // https://hyperelliptic.org/EFD/g1p/auto-shortw-jacobian-0.html#addition-madd-2007-bl + // + // In particular it performs the calculations using the following: + // Z1Z1 = Z1^2, U2 = X2*Z1Z1, S2 = Y2*Z1*Z1Z1, H = U2-X1, HH = H^2, + // I = 4*HH, J = H*I, r = 2*(S2-Y1), V = X1*I + // X3 = r^2-J-2*V, Y3 = r*(V-X3)-2*Y1*J, Z3 = (Z1+H)^2-Z1Z1-HH + // + // This results in a cost of 7 field multiplications, 4 field squarings, + // 9 field additions, and 4 integer multiplications. + x1, y1, z1 := &p1.X, &p1.Y, &p1.Z + x2, y2 := &p2.X, &p2.Y + x3, y3, z3 := &result.X, &result.Y, &result.Z + + // When the x coordinates are the same for two points on the curve, the + // y coordinates either must be the same, in which case it is point + // doubling, or they are opposite and the result is the point at + // infinity per the group law for elliptic curve cryptography. Since + // any number of Jacobian coordinates can represent the same affine + // point, the x and y values need to be converted to like terms. Due to + // the assumption made for this function that the second point has a z + // value of 1 (z2=1), the first point is already "converted". + var z1z1, u2, s2 FieldVal + z1z1.SquareVal(z1) // Z1Z1 = Z1^2 (mag: 1) + u2.Set(x2).Mul(&z1z1).Normalize() // U2 = X2*Z1Z1 (mag: 1) + s2.Set(y2).Mul(&z1z1).Mul(z1).Normalize() // S2 = Y2*Z1*Z1Z1 (mag: 1) + if x1.Equals(&u2) { + if y1.Equals(&s2) { + // Since x1 == x2 and y1 == y2, point doubling must be + // done, otherwise the addition would end up dividing + // by zero. + DoubleNonConst(p1, result) + return + } + + // Since x1 == x2 and y1 == -y2, the sum is the point at + // infinity per the group law. + x3.SetInt(0) + y3.SetInt(0) + z3.SetInt(0) + return + } + + // Calculate X3, Y3, and Z3 according to the intermediate elements + // breakdown above. + var h, hh, i, j, r, rr, v FieldVal + var negX1, negY1, negX3 FieldVal + negX1.Set(x1).Negate(1) // negX1 = -X1 (mag: 2) + h.Add2(&u2, &negX1) // H = U2-X1 (mag: 3) + hh.SquareVal(&h) // HH = H^2 (mag: 1) + i.Set(&hh).MulInt(4) // I = 4 * HH (mag: 4) + j.Mul2(&h, &i) // J = H*I (mag: 1) + negY1.Set(y1).Negate(1) // negY1 = -Y1 (mag: 2) + r.Set(&s2).Add(&negY1).MulInt(2) // r = 2*(S2-Y1) (mag: 6) + rr.SquareVal(&r) // rr = r^2 (mag: 1) + v.Mul2(x1, &i) // V = X1*I (mag: 1) + x3.Set(&v).MulInt(2).Add(&j).Negate(3) // X3 = -(J+2*V) (mag: 4) + x3.Add(&rr) // X3 = r^2+X3 (mag: 5) + negX3.Set(x3).Negate(5) // negX3 = -X3 (mag: 6) + y3.Set(y1).Mul(&j).MulInt(2).Negate(2) // Y3 = -(2*Y1*J) (mag: 3) + y3.Add(v.Add(&negX3).Mul(&r)) // Y3 = r*(V-X3)+Y3 (mag: 4) + z3.Add2(z1, &h).Square() // Z3 = (Z1+H)^2 (mag: 1) + z3.Add(z1z1.Add(&hh).Negate(2)) // Z3 = Z3-(Z1Z1+HH) (mag: 4) + + // Normalize the resulting field values to a magnitude of 1 as needed. + x3.Normalize() + y3.Normalize() + z3.Normalize() +} + +// addGeneric adds two Jacobian points without any assumptions about the z +// values of the two points and stores the result in the provided result param. +// That is to say result = p1 + p2. It is the slowest of the add routines due +// to requiring the most arithmetic. +// +// NOTE: The points must be normalized for this function to return the correct +// result. The resulting point will be normalized. +func addGeneric(p1, p2, result *JacobianPoint) { + // To compute the point addition efficiently, this implementation splits + // the equation into intermediate elements which are used to minimize + // the number of field multiplications using the method shown at: + // https://hyperelliptic.org/EFD/g1p/auto-shortw-jacobian-0.html#addition-add-2007-bl + // + // In particular it performs the calculations using the following: + // Z1Z1 = Z1^2, Z2Z2 = Z2^2, U1 = X1*Z2Z2, U2 = X2*Z1Z1, S1 = Y1*Z2*Z2Z2 + // S2 = Y2*Z1*Z1Z1, H = U2-U1, I = (2*H)^2, J = H*I, r = 2*(S2-S1) + // V = U1*I + // X3 = r^2-J-2*V, Y3 = r*(V-X3)-2*S1*J, Z3 = ((Z1+Z2)^2-Z1Z1-Z2Z2)*H + // + // This results in a cost of 11 field multiplications, 5 field squarings, + // 9 field additions, and 4 integer multiplications. + x1, y1, z1 := &p1.X, &p1.Y, &p1.Z + x2, y2, z2 := &p2.X, &p2.Y, &p2.Z + x3, y3, z3 := &result.X, &result.Y, &result.Z + + // When the x coordinates are the same for two points on the curve, the + // y coordinates either must be the same, in which case it is point + // doubling, or they are opposite and the result is the point at + // infinity. Since any number of Jacobian coordinates can represent the + // same affine point, the x and y values need to be converted to like + // terms. + var z1z1, z2z2, u1, u2, s1, s2 FieldVal + z1z1.SquareVal(z1) // Z1Z1 = Z1^2 (mag: 1) + z2z2.SquareVal(z2) // Z2Z2 = Z2^2 (mag: 1) + u1.Set(x1).Mul(&z2z2).Normalize() // U1 = X1*Z2Z2 (mag: 1) + u2.Set(x2).Mul(&z1z1).Normalize() // U2 = X2*Z1Z1 (mag: 1) + s1.Set(y1).Mul(&z2z2).Mul(z2).Normalize() // S1 = Y1*Z2*Z2Z2 (mag: 1) + s2.Set(y2).Mul(&z1z1).Mul(z1).Normalize() // S2 = Y2*Z1*Z1Z1 (mag: 1) + if u1.Equals(&u2) { + if s1.Equals(&s2) { + // Since x1 == x2 and y1 == y2, point doubling must be + // done, otherwise the addition would end up dividing + // by zero. + DoubleNonConst(p1, result) + return + } + + // Since x1 == x2 and y1 == -y2, the sum is the point at + // infinity per the group law. + x3.SetInt(0) + y3.SetInt(0) + z3.SetInt(0) + return + } + + // Calculate X3, Y3, and Z3 according to the intermediate elements + // breakdown above. + var h, i, j, r, rr, v FieldVal + var negU1, negS1, negX3 FieldVal + negU1.Set(&u1).Negate(1) // negU1 = -U1 (mag: 2) + h.Add2(&u2, &negU1) // H = U2-U1 (mag: 3) + i.Set(&h).MulInt(2).Square() // I = (2*H)^2 (mag: 2) + j.Mul2(&h, &i) // J = H*I (mag: 1) + negS1.Set(&s1).Negate(1) // negS1 = -S1 (mag: 2) + r.Set(&s2).Add(&negS1).MulInt(2) // r = 2*(S2-S1) (mag: 6) + rr.SquareVal(&r) // rr = r^2 (mag: 1) + v.Mul2(&u1, &i) // V = U1*I (mag: 1) + x3.Set(&v).MulInt(2).Add(&j).Negate(3) // X3 = -(J+2*V) (mag: 4) + x3.Add(&rr) // X3 = r^2+X3 (mag: 5) + negX3.Set(x3).Negate(5) // negX3 = -X3 (mag: 6) + y3.Mul2(&s1, &j).MulInt(2).Negate(2) // Y3 = -(2*S1*J) (mag: 3) + y3.Add(v.Add(&negX3).Mul(&r)) // Y3 = r*(V-X3)+Y3 (mag: 4) + z3.Add2(z1, z2).Square() // Z3 = (Z1+Z2)^2 (mag: 1) + z3.Add(z1z1.Add(&z2z2).Negate(2)) // Z3 = Z3-(Z1Z1+Z2Z2) (mag: 4) + z3.Mul(&h) // Z3 = Z3*H (mag: 1) + + // Normalize the resulting field values to a magnitude of 1 as needed. + x3.Normalize() + y3.Normalize() + z3.Normalize() +} + +// AddNonConst adds the passed Jacobian points together and stores the result in +// the provided result param in *non-constant* time. +// +// NOTE: The points must be normalized for this function to return the correct +// result. The resulting point will be normalized. +func AddNonConst(p1, p2, result *JacobianPoint) { + // A point at infinity is the identity according to the group law for + // elliptic curve cryptography. Thus, ∞ + P = P and P + ∞ = P. + if (p1.X.IsZero() && p1.Y.IsZero()) || p1.Z.IsZero() { + result.Set(p2) + return + } + if (p2.X.IsZero() && p2.Y.IsZero()) || p2.Z.IsZero() { + result.Set(p1) + return + } + + // Faster point addition can be achieved when certain assumptions are + // met. For example, when both points have the same z value, arithmetic + // on the z values can be avoided. This section thus checks for these + // conditions and calls an appropriate add function which is accelerated + // by using those assumptions. + isZ1One := p1.Z.IsOne() + isZ2One := p2.Z.IsOne() + switch { + case isZ1One && isZ2One: + addZ1AndZ2EqualsOne(p1, p2, result) + return + case p1.Z.Equals(&p2.Z): + addZ1EqualsZ2(p1, p2, result) + return + case isZ2One: + addZ2EqualsOne(p1, p2, result) + return + } + + // None of the above assumptions are true, so fall back to generic + // point addition. + addGeneric(p1, p2, result) +} + +// doubleZ1EqualsOne performs point doubling on the passed Jacobian point when +// the point is already known to have a z value of 1 and stores the result in +// the provided result param. That is to say result = 2*p. It performs faster +// point doubling than the generic routine since less arithmetic is needed due +// to the ability to avoid multiplication by the z value. +// +// NOTE: The resulting point will be normalized. +func doubleZ1EqualsOne(p, result *JacobianPoint) { + // This function uses the assumptions that z1 is 1, thus the point + // doubling formulas reduce to: + // + // X3 = (3*X1^2)^2 - 8*X1*Y1^2 + // Y3 = (3*X1^2)*(4*X1*Y1^2 - X3) - 8*Y1^4 + // Z3 = 2*Y1 + // + // To compute the above efficiently, this implementation splits the + // equation into intermediate elements which are used to minimize the + // number of field multiplications in favor of field squarings which + // are roughly 35% faster than field multiplications with the current + // implementation at the time this was written. + // + // This uses a slightly modified version of the method shown at: + // https://hyperelliptic.org/EFD/g1p/auto-shortw-jacobian-0.html#doubling-mdbl-2007-bl + // + // In particular it performs the calculations using the following: + // A = X1^2, B = Y1^2, C = B^2, D = 2*((X1+B)^2-A-C) + // E = 3*A, F = E^2, X3 = F-2*D, Y3 = E*(D-X3)-8*C + // Z3 = 2*Y1 + // + // This results in a cost of 1 field multiplication, 5 field squarings, + // 6 field additions, and 5 integer multiplications. + x1, y1 := &p.X, &p.Y + x3, y3, z3 := &result.X, &result.Y, &result.Z + var a, b, c, d, e, f FieldVal + z3.Set(y1).MulInt(2) // Z3 = 2*Y1 (mag: 2) + a.SquareVal(x1) // A = X1^2 (mag: 1) + b.SquareVal(y1) // B = Y1^2 (mag: 1) + c.SquareVal(&b) // C = B^2 (mag: 1) + b.Add(x1).Square() // B = (X1+B)^2 (mag: 1) + d.Set(&a).Add(&c).Negate(2) // D = -(A+C) (mag: 3) + d.Add(&b).MulInt(2) // D = 2*(B+D)(mag: 8) + e.Set(&a).MulInt(3) // E = 3*A (mag: 3) + f.SquareVal(&e) // F = E^2 (mag: 1) + x3.Set(&d).MulInt(2).Negate(16) // X3 = -(2*D) (mag: 17) + x3.Add(&f) // X3 = F+X3 (mag: 18) + f.Set(x3).Negate(18).Add(&d).Normalize() // F = D-X3 (mag: 1) + y3.Set(&c).MulInt(8).Negate(8) // Y3 = -(8*C) (mag: 9) + y3.Add(f.Mul(&e)) // Y3 = E*F+Y3 (mag: 10) + + // Normalize the field values back to a magnitude of 1. + x3.Normalize() + y3.Normalize() + z3.Normalize() +} + +// doubleGeneric performs point doubling on the passed Jacobian point without +// any assumptions about the z value and stores the result in the provided +// result param. That is to say result = 2*p. It is the slowest of the point +// doubling routines due to requiring the most arithmetic. +// +// NOTE: The resulting point will be normalized. +func doubleGeneric(p, result *JacobianPoint) { + // Point doubling formula for Jacobian coordinates for the secp256k1 + // curve: + // + // X3 = (3*X1^2)^2 - 8*X1*Y1^2 + // Y3 = (3*X1^2)*(4*X1*Y1^2 - X3) - 8*Y1^4 + // Z3 = 2*Y1*Z1 + // + // To compute the above efficiently, this implementation splits the + // equation into intermediate elements which are used to minimize the + // number of field multiplications in favor of field squarings which + // are roughly 35% faster than field multiplications with the current + // implementation at the time this was written. + // + // This uses a slightly modified version of the method shown at: + // https://hyperelliptic.org/EFD/g1p/auto-shortw-jacobian-0.html#doubling-dbl-2009-l + // + // In particular it performs the calculations using the following: + // A = X1^2, B = Y1^2, C = B^2, D = 2*((X1+B)^2-A-C) + // E = 3*A, F = E^2, X3 = F-2*D, Y3 = E*(D-X3)-8*C + // Z3 = 2*Y1*Z1 + // + // This results in a cost of 1 field multiplication, 5 field squarings, + // 6 field additions, and 5 integer multiplications. + x1, y1, z1 := &p.X, &p.Y, &p.Z + x3, y3, z3 := &result.X, &result.Y, &result.Z + var a, b, c, d, e, f FieldVal + z3.Mul2(y1, z1).MulInt(2) // Z3 = 2*Y1*Z1 (mag: 2) + a.SquareVal(x1) // A = X1^2 (mag: 1) + b.SquareVal(y1) // B = Y1^2 (mag: 1) + c.SquareVal(&b) // C = B^2 (mag: 1) + b.Add(x1).Square() // B = (X1+B)^2 (mag: 1) + d.Set(&a).Add(&c).Negate(2) // D = -(A+C) (mag: 3) + d.Add(&b).MulInt(2) // D = 2*(B+D)(mag: 8) + e.Set(&a).MulInt(3) // E = 3*A (mag: 3) + f.SquareVal(&e) // F = E^2 (mag: 1) + x3.Set(&d).MulInt(2).Negate(16) // X3 = -(2*D) (mag: 17) + x3.Add(&f) // X3 = F+X3 (mag: 18) + f.Set(x3).Negate(18).Add(&d).Normalize() // F = D-X3 (mag: 1) + y3.Set(&c).MulInt(8).Negate(8) // Y3 = -(8*C) (mag: 9) + y3.Add(f.Mul(&e)) // Y3 = E*F+Y3 (mag: 10) + + // Normalize the field values back to a magnitude of 1. + x3.Normalize() + y3.Normalize() + z3.Normalize() +} + +// DoubleNonConst doubles the passed Jacobian point and stores the result in the +// provided result parameter in *non-constant* time. +// +// NOTE: The point must be normalized for this function to return the correct +// result. The resulting point will be normalized. +func DoubleNonConst(p, result *JacobianPoint) { + // Doubling a point at infinity is still infinity. + if p.Y.IsZero() || p.Z.IsZero() { + result.X.SetInt(0) + result.Y.SetInt(0) + result.Z.SetInt(0) + return + } + + // Slightly faster point doubling can be achieved when the z value is 1 + // by avoiding the multiplication on the z value. This section calls + // a point doubling function which is accelerated by using that + // assumption when possible. + if p.Z.IsOne() { + doubleZ1EqualsOne(p, result) + return + } + + // Fall back to generic point doubling which works with arbitrary z + // values. + doubleGeneric(p, result) +} + +// splitK returns a balanced length-two representation of k and their signs. +// This is algorithm 3.74 from [GECC]. +// +// One thing of note about this algorithm is that no matter what c1 and c2 are, +// the final equation of k = k1 + k2 * lambda (mod n) will hold. This is +// provable mathematically due to how a1/b1/a2/b2 are computed. +// +// c1 and c2 are chosen to minimize the max(k1,k2). +func splitK(k []byte) ([]byte, []byte, int, int) { + // All math here is done with big.Int, which is slow. + // At some point, it might be useful to write something similar to + // FieldVal but for N instead of P as the prime field if this ends up + // being a bottleneck. + bigIntK := new(big.Int) + c1, c2 := new(big.Int), new(big.Int) + tmp1, tmp2 := new(big.Int), new(big.Int) + k1, k2 := new(big.Int), new(big.Int) + + bigIntK.SetBytes(k) + // c1 = round(b2 * k / n) from step 4. + // Rounding isn't really necessary and costs too much, hence skipped + c1.Mul(endomorphismB2, bigIntK) + c1.Div(c1, curveParams.N) + // c2 = round(b1 * k / n) from step 4 (sign reversed to optimize one step) + // Rounding isn't really necessary and costs too much, hence skipped + c2.Mul(endomorphismB1, bigIntK) + c2.Div(c2, curveParams.N) + // k1 = k - c1 * a1 - c2 * a2 from step 5 (note c2's sign is reversed) + tmp1.Mul(c1, endomorphismA1) + tmp2.Mul(c2, endomorphismA2) + k1.Sub(bigIntK, tmp1) + k1.Add(k1, tmp2) + // k2 = - c1 * b1 - c2 * b2 from step 5 (note c2's sign is reversed) + tmp1.Mul(c1, endomorphismB1) + tmp2.Mul(c2, endomorphismB2) + k2.Sub(tmp2, tmp1) + + // Note Bytes() throws out the sign of k1 and k2. This matters + // since k1 and/or k2 can be negative. Hence, we pass that + // back separately. + return k1.Bytes(), k2.Bytes(), k1.Sign(), k2.Sign() +} + +// nafScalar represents a positive integer up to a maximum value of 2^256 - 1 +// encoded in non-adjacent form. +// +// NAF is a signed-digit representation where each digit can be +1, 0, or -1. +// +// In order to efficiently encode that information, this type uses two arrays, a +// "positive" array where set bits represent the +1 signed digits and a +// "negative" array where set bits represent the -1 signed digits. 0 is +// represented by neither array having a bit set in that position. +// +// The Pos and Neg methods return the aforementioned positive and negative +// arrays, respectively. +type nafScalar struct { + // pos houses the positive portion of the representation. An additional + // byte is required for the positive portion because the NAF encoding can be + // up to 1 bit longer than the normal binary encoding of the value. + // + // neg houses the negative portion of the representation. Even though the + // additional byte is not required for the negative portion, since it can + // never exceed the length of the normal binary encoding of the value, + // keeping the same length for positive and negative portions simplifies + // working with the representation and allows extra conditional branches to + // be avoided. + // + // start and end specify the starting and ending index to use within the pos + // and neg arrays, respectively. This allows fixed size arrays to be used + // versus needing to dynamically allocate space on the heap. + // + // NOTE: The fields are defined in the order that they are to minimize the + // padding on 32-bit and 64-bit platforms. + pos [33]byte + start, end uint8 + neg [33]byte +} + +// Pos returns the bytes of the encoded value with bits set in the positions +// that represent a signed digit of +1. +func (s *nafScalar) Pos() []byte { + return s.pos[s.start:s.end] +} + +// Neg returns the bytes of the encoded value with bits set in the positions +// that represent a signed digit of -1. +func (s *nafScalar) Neg() []byte { + return s.neg[s.start:s.end] +} + +// naf takes a positive integer up to a maximum value of 2^256 - 1 and returns +// its non-adjacent form (NAF), which is a unique signed-digit representation +// such that no two consecutive digits are nonzero. See the documentation for +// the returned type for details on how the representation is encoded +// efficiently and how to interpret it +// +// NAF is useful in that it has the fewest nonzero digits of any signed digit +// representation, only 1/3rd of its digits are nonzero on average, and at least +// half of the digits will be 0. +// +// The aforementioned properties are particularly beneficial for optimizing +// elliptic curve point multiplication because they effectively minimize the +// number of required point additions in exchange for needing to perform a mix +// of fewer point additions and subtractions and possibly one additional point +// doubling. This is an excellent tradeoff because subtraction of points has +// the same computational complexity as addition of points and point doubling is +// faster than both. +func naf(k []byte) nafScalar { + // Strip leading zero bytes. + for len(k) > 0 && k[0] == 0x00 { + k = k[1:] + } + + // The non-adjacent form (NAF) of a positive integer k is an expression + // k = ∑_(i=0, l-1) k_i * 2^i where k_i ∈ {0,±1}, k_(l-1) != 0, and no two + // consecutive digits k_i are nonzero. + // + // The traditional method of computing the NAF of a positive integer is + // given by algorithm 3.30 in [GECC]. It consists of repeatedly dividing k + // by 2 and choosing the remainder so that the quotient (k−r)/2 is even + // which ensures the next NAF digit is 0. This requires log_2(k) steps. + // + // However, in [BRID], Prodinger notes that a closed form expression for the + // NAF representation is the bitwise difference 3k/2 - k/2. This is more + // efficient as it can be computed in O(1) versus the O(log(n)) of the + // traditional approach. + // + // The following code makes use of that formula to compute the NAF more + // efficiently. + // + // To understand the logic here, observe that the only way the NAF has a + // nonzero digit at a given bit is when either 3k/2 or k/2 has a bit set in + // that position, but not both. In other words, the result of a bitwise + // xor. This can be seen simply by considering that when the bits are the + // same, the subtraction is either 0-0 or 1-1, both of which are 0. + // + // Further, observe that the "+1" digits in the result are contributed by + // 3k/2 while the "-1" digits are from k/2. So, they can be determined by + // taking the bitwise and of each respective value with the result of the + // xor which identifies which bits are nonzero. + // + // Using that information, this loops backwards from the least significant + // byte to the most significant byte while performing the aforementioned + // calculations by propagating the potential carry and high order bit from + // the next word during the right shift. + kLen := len(k) + var result nafScalar + var carry uint8 + for byteNum := kLen - 1; byteNum >= 0; byteNum-- { + // Calculate k/2. Notice the carry from the previous word is added and + // the low order bit from the next word is shifted in accordingly. + kc := uint16(k[byteNum]) + uint16(carry) + var nextWord uint8 + if byteNum > 0 { + nextWord = k[byteNum-1] + } + halfK := kc>>1 | uint16(nextWord<<7) + + // Calculate 3k/2 and determine the non-zero digits in the result. + threeHalfK := kc + halfK + nonZeroResultDigits := threeHalfK ^ halfK + + // Determine the signed digits {0, ±1}. + result.pos[byteNum+1] = uint8(threeHalfK & nonZeroResultDigits) + result.neg[byteNum+1] = uint8(halfK & nonZeroResultDigits) + + // Propagate the potential carry from the 3k/2 calculation. + carry = uint8(threeHalfK >> 8) + } + result.pos[0] = carry + + // Set the starting and ending positions within the fixed size arrays to + // identify the bytes that are actually used. This is important since the + // encoding is big endian and thus trailing zero bytes changes its value. + result.start = 1 - carry + result.end = uint8(kLen + 1) + return result +} + +// ScalarMultNonConst multiplies k*P where k is a big endian integer modulo the +// curve order and P is a point in Jacobian projective coordinates and stores +// the result in the provided Jacobian point. +// +// NOTE: The point must be normalized for this function to return the correct +// result. The resulting point will be normalized. +func ScalarMultNonConst(k *ModNScalar, point, result *JacobianPoint) { + // Decompose K into k1 and k2 in order to halve the number of EC ops. + // See Algorithm 3.74 in [GECC]. + kBytes := k.Bytes() + k1, k2, signK1, signK2 := splitK(kBytes[:]) + zeroArray32(&kBytes) + + // The main equation here to remember is: + // k * P = k1 * P + k2 * ϕ(P) + // + // P1 below is P in the equation, P2 below is ϕ(P) in the equation + p1, p1Neg := new(JacobianPoint), new(JacobianPoint) + p1.Set(point) + p1Neg.Set(p1) + p1Neg.Y.Negate(1).Normalize() + + // NOTE: ϕ(x,y) = (βx,y). The Jacobian z coordinates are the same, so this + // math goes through. + p2, p2Neg := new(JacobianPoint), new(JacobianPoint) + p2.Set(p1) + p2.X.Mul(endomorphismBeta).Normalize() + p2Neg.Set(p2) + p2Neg.Y.Negate(1).Normalize() + + // Flip the positive and negative values of the points as needed + // depending on the signs of k1 and k2. As mentioned in the equation + // above, each of k1 and k2 are multiplied by the respective point. + // Since -k * P is the same thing as k * -P, and the group law for + // elliptic curves states that P(x, y) = -P(x, -y), it's faster and + // simplifies the code to just make the point negative. + if signK1 == -1 { + p1, p1Neg = p1Neg, p1 + } + if signK2 == -1 { + p2, p2Neg = p2Neg, p2 + } + + // NAF versions of k1 and k2 should have a lot more zeros. + // + // The Pos version of the bytes contain the +1s and the Neg versions + // contain the -1s. + k1NAF, k2NAF := naf(k1), naf(k2) + k1PosNAF, k1NegNAF := k1NAF.Pos(), k1NAF.Neg() + k2PosNAF, k2NegNAF := k2NAF.Pos(), k2NAF.Neg() + k1Len, k2Len := len(k1PosNAF), len(k2PosNAF) + + m := k1Len + if m < k2Len { + m = k2Len + } + + // Point Q = ∞ (point at infinity). + var q JacobianPoint + + // Add left-to-right using the NAF optimization. See algorithm 3.77 + // from [GECC]. This should be faster overall since there will be a lot + // more instances of 0, hence reducing the number of Jacobian additions + // at the cost of 1 possible extra doubling. + for i := 0; i < m; i++ { + // Since k1 and k2 are potentially different lengths and the calculation + // is being done left to right, pad the front of the shorter one with + // 0s. + var k1BytePos, k1ByteNeg, k2BytePos, k2ByteNeg byte + if i >= m-k1Len { + k1BytePos, k1ByteNeg = k1PosNAF[i-(m-k1Len)], k1NegNAF[i-(m-k1Len)] + } + if i >= m-k2Len { + k2BytePos, k2ByteNeg = k2PosNAF[i-(m-k2Len)], k2NegNAF[i-(m-k2Len)] + } + for bit, mask := 7, uint8(1<<7); bit >= 0; bit, mask = bit-1, mask>>1 { + // Q = 2 * Q + DoubleNonConst(&q, &q) + + // Add or subtract the first point based on the signed digit of the + // NAF representation of k1 at this bit position. + // + // +1: Q = Q + p1 + // -1: Q = Q - p1 + // 0: Q = Q (no change) + if k1BytePos&mask == mask { + AddNonConst(&q, p1, &q) + } else if k1ByteNeg&mask == mask { + AddNonConst(&q, p1Neg, &q) + } + + // Add or subtract the second point based on the signed digit of the + // NAF representation of k2 at this bit position. + // + // +1: Q = Q + p2 + // -1: Q = Q - p2 + // 0: Q = Q (no change) + if k2BytePos&mask == mask { + AddNonConst(&q, p2, &q) + } else if k2ByteNeg&mask == mask { + AddNonConst(&q, p2Neg, &q) + } + } + } + + result.Set(&q) +} + +// ScalarBaseMultNonConst multiplies k*G where G is the base point of the group +// and k is a big endian integer. The result is stored in Jacobian coordinates +// (x1, y1, z1). +// +// NOTE: The resulting point will be normalized. +func ScalarBaseMultNonConst(k *ModNScalar, result *JacobianPoint) { + bytePoints := s256BytePoints() + + // Point Q = ∞ (point at infinity). + var q JacobianPoint + + // curve.bytePoints has all 256 byte points for each 8-bit window. The + // strategy is to add up the byte points. This is best understood by + // expressing k in base-256 which it already sort of is. Each "digit" in + // the 8-bit window can be looked up using bytePoints and added together. + var pt JacobianPoint + for i, byteVal := range k.Bytes() { + p := bytePoints[i][byteVal] + pt.X.Set(&p[0]) + pt.Y.Set(&p[1]) + pt.Z.SetInt(1) + AddNonConst(&q, &pt, &q) + } + + result.Set(&q) +} + +// isOnCurve returns whether or not the affine point (x,y) is on the curve. +func isOnCurve(fx, fy *FieldVal) bool { + // Elliptic curve equation for secp256k1 is: y^2 = x^3 + 7 + y2 := new(FieldVal).SquareVal(fy).Normalize() + result := new(FieldVal).SquareVal(fx).Mul(fx).AddInt(7).Normalize() + return y2.Equals(result) +} + +// DecompressY attempts to calculate the Y coordinate for the given X coordinate +// such that the result pair is a point on the secp256k1 curve. It adjusts Y +// based on the desired oddness and returns whether or not it was successful +// since not all X coordinates are valid. +// +// The magnitude of the provided X coordinate field val must be a max of 8 for a +// correct result. The resulting Y field val will have a max magnitude of 2. +func DecompressY(x *FieldVal, odd bool, resultY *FieldVal) bool { + // The curve equation for secp256k1 is: y^2 = x^3 + 7. Thus + // y = +-sqrt(x^3 + 7). + // + // The x coordinate must be invalid if there is no square root for the + // calculated rhs because it means the X coordinate is not for a point on + // the curve. + x3PlusB := new(FieldVal).SquareVal(x).Mul(x).AddInt(7) + if hasSqrt := resultY.SquareRootVal(x3PlusB); !hasSqrt { + return false + } + if resultY.Normalize().IsOdd() != odd { + resultY.Negate(1) + } + return true +} diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/doc.go b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/doc.go new file mode 100644 index 0000000..91a670e --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/doc.go @@ -0,0 +1,58 @@ +// Copyright (c) 2013-2014 The btcsuite developers +// Copyright (c) 2015-2019 The Decred developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +/* +Package secp256k1 implements optimized secp256k1 elliptic curve operations. + +This package provides an optimized pure Go implementation of elliptic curve +cryptography operations over the secp256k1 curve as well as data structures and +functions for working with public and private secp256k1 keys. See +https://www.secg.org/sec2-v2.pdf for details on the standard. + +In addition, sub packages are provided to produce, verify, parse, and serialize +ECDSA signatures and EC-Schnorr-DCRv0 (a custom Schnorr-based signature scheme +specific to Decred) signatures. See the README.md files in the relevant sub +packages for more details about those aspects. + +An overview of the features provided by this package are as follows: + + - Private key generation, serialization, and parsing + - Public key generation, serialization and parsing per ANSI X9.62-1998 + - Parses uncompressed, compressed, and hybrid public keys + - Serializes uncompressed and compressed public keys + - Specialized types for performing optimized and constant time field operations + - FieldVal type for working modulo the secp256k1 field prime + - ModNScalar type for working modulo the secp256k1 group order + - Elliptic curve operations in Jacobian projective coordinates + - Point addition + - Point doubling + - Scalar multiplication with an arbitrary point + - Scalar multiplication with the base point (group generator) + - Point decompression from a given x coordinate + - Nonce generation via RFC6979 with support for extra data and version + information that can be used to prevent nonce reuse between signing + algorithms + +It also provides an implementation of the Go standard library crypto/elliptic +Curve interface via the S256 function so that it may be used with other packages +in the standard library such as crypto/tls, crypto/x509, and crypto/ecdsa. +However, in the case of ECDSA, it is highly recommended to use the ecdsa sub +package of this package instead since it is optimized specifically for secp256k1 +and is significantly faster as a result. + +Although this package was primarily written for dcrd, it has intentionally been +designed so it can be used as a standalone package for any projects needing to +use optimized secp256k1 elliptic curve cryptography. + +Finally, a comprehensive suite of tests is provided to provide a high level of +quality assurance. + +Use of secp256k1 in Decred + +At the time of this writing, the primary public key cryptography in widespread +use on the Decred network used to secure coins is based on elliptic curves +defined by the secp256k1 domain parameters. +*/ +package secp256k1 diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/ecdh.go b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/ecdh.go new file mode 100644 index 0000000..ebbdfc5 --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/ecdh.go @@ -0,0 +1,21 @@ +// Copyright (c) 2015 The btcsuite developers +// Copyright (c) 2015-2016 The Decred developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +package secp256k1 + +// GenerateSharedSecret generates a shared secret based on a private key and a +// public key using Diffie-Hellman key exchange (ECDH) (RFC 5903). +// RFC5903 Section 9 states we should only return x. +// +// It is recommended to securily hash the result before using as a cryptographic +// key. +func GenerateSharedSecret(privkey *PrivateKey, pubkey *PublicKey) []byte { + var point, result JacobianPoint + pubkey.AsJacobian(&point) + ScalarMultNonConst(&privkey.Key, &point, &result) + result.ToAffine() + xBytes := result.X.Bytes() + return xBytes[:] +} diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/ecdsa/README.md b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/ecdsa/README.md new file mode 100644 index 0000000..cc3c0aa --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/ecdsa/README.md @@ -0,0 +1,52 @@ +ecdsa +===== + +[![Build Status](https://github.com/decred/dcrd/workflows/Build%20and%20Test/badge.svg)](https://github.com/decred/dcrd/actions) +[![ISC License](https://img.shields.io/badge/license-ISC-blue.svg)](http://copyfree.org) +[![GoDoc](https://img.shields.io/badge/godoc-reference-blue.svg)](https://pkg.go.dev/github.com/decred/dcrd/dcrec/secp256k1/v4/ecdsa) + +Package ecdsa provides secp256k1-optimized ECDSA signing and verification. + +This package provides data structures and functions necessary to produce and +verify deterministic canonical signatures in accordance with RFC6979 and +BIP0062, optimized specifically for the secp256k1 curve using the Elliptic Curve +Digital Signature Algorithm (ECDSA), as defined in FIPS 186-3. See +https://www.secg.org/sec2-v2.pdf for details on the secp256k1 standard. + +It also provides functions to parse and serialize the ECDSA signatures with the +more strict Distinguished Encoding Rules (DER) of ISO/IEC 8825-1 and some +additional restrictions specific to secp256k1. + +In addition, it supports a custom "compact" signature format which allows +efficient recovery of the public key from a given valid signature and message +hash combination. + +A comprehensive suite of tests is provided to ensure proper functionality. + +## ECDSA use in Decred + +At the time of this writing, ECDSA signatures are heavily used for proving coin +ownership in Decred as the vast majority of transactions consist of what is +effectively transferring ownership of coins to a public key associated with a +private key only known to the recipient of the coins along with an encumbrance +that requires an ECDSA signature that proves the new owner possesses the private +key without actually revealing it. + +## Installation and Updating + +This package is part of the `github.com/decred/dcrd/dcrec/secp256k1/v4` module. +Use the standard go tooling for working with modules to incorporate it. + +## Examples + +* [Sign Message](https://pkg.go.dev/github.com/decred/dcrd/dcrec/secp256k1/v4/ecdsa#example-package-SignMessage) + Demonstrates signing a message with a secp256k1 private key that is first + parsed from raw bytes and serializing the generated signature. + +* [Verify Signature](https://pkg.go.dev/github.com/decred/dcrd/dcrec/secp256k1/v4/ecdsa#example-Signature.Verify) + Demonstrates verifying a secp256k1 signature against a public key that is + first parsed from raw bytes. The signature is also parsed from raw bytes. + +## License + +Package ecdsa is licensed under the [copyfree](http://copyfree.org) ISC License. diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/ecdsa/doc.go b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/ecdsa/doc.go new file mode 100644 index 0000000..14f38ef --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/ecdsa/doc.go @@ -0,0 +1,42 @@ +// Copyright (c) 2020 The Decred developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +/* +Package ecdsa provides secp256k1-optimized ECDSA signing and verification. + +This package provides data structures and functions necessary to produce and +verify deterministic canonical signatures in accordance with RFC6979 and +BIP0062, optimized specifically for the secp256k1 curve using the Elliptic Curve +Digital Signature Algorithm (ECDSA), as defined in FIPS 186-3. See +https://www.secg.org/sec2-v2.pdf for details on the secp256k1 standard. + +It also provides functions to parse and serialize the ECDSA signatures with the +more strict Distinguished Encoding Rules (DER) of ISO/IEC 8825-1 and some +additional restrictions specific to secp256k1. + +In addition, it supports a custom "compact" signature format which allows +efficient recovery of the public key from a given valid signature and message +hash combination. + +A comprehensive suite of tests is provided to ensure proper functionality. + +ECDSA use in Decred + +At the time of this writing, ECDSA signatures are heavily used for proving coin +ownership in Decred as the vast majority of transactions consist of what is +effectively transferring ownership of coins to a public key associated with a +private key only known to the recipient of the coins along with an encumbrance +that requires an ECDSA signature that proves the new owner possesses the private +key without actually revealing it. + +Errors + +Errors returned by this package are of type ecdsa.Error and fully support the +standard library errors.Is and errors.As functions. This allows the caller to +programmatically determine the specific error by examining the ErrorKind field +of the type asserted ecdsa.Error while still providing rich error messages with +contextual information. See ErrorKind in the package documentation for a full +list. +*/ +package ecdsa diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/ecdsa/error.go b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/ecdsa/error.go new file mode 100644 index 0000000..45f8252 --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/ecdsa/error.go @@ -0,0 +1,116 @@ +// Copyright (c) 2020 The Decred developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +package ecdsa + +// ErrorKind identifies a kind of error. It has full support for +// errors.Is and errors.As, so the caller can directly check against +// an error kind when determining the reason for an error. +type ErrorKind string + +// These constants are used to identify a specific Error. +const ( + // ErrSigTooShort is returned when a signature that should be a DER + // signature is too short. + ErrSigTooShort = ErrorKind("ErrSigTooShort") + + // ErrSigTooLong is returned when a signature that should be a DER signature + // is too long. + ErrSigTooLong = ErrorKind("ErrSigTooLong") + + // ErrSigInvalidSeqID is returned when a signature that should be a DER + // signature does not have the expected ASN.1 sequence ID. + ErrSigInvalidSeqID = ErrorKind("ErrSigInvalidSeqID") + + // ErrSigInvalidDataLen is returned when a signature that should be a DER + // signature does not specify the correct number of remaining bytes for the + // R and S portions. + ErrSigInvalidDataLen = ErrorKind("ErrSigInvalidDataLen") + + // ErrSigMissingSTypeID is returned when a signature that should be a DER + // signature does not provide the ASN.1 type ID for S. + ErrSigMissingSTypeID = ErrorKind("ErrSigMissingSTypeID") + + // ErrSigMissingSLen is returned when a signature that should be a DER + // signature does not provide the length of S. + ErrSigMissingSLen = ErrorKind("ErrSigMissingSLen") + + // ErrSigInvalidSLen is returned when a signature that should be a DER + // signature does not specify the correct number of bytes for the S portion. + ErrSigInvalidSLen = ErrorKind("ErrSigInvalidSLen") + + // ErrSigInvalidRIntID is returned when a signature that should be a DER + // signature does not have the expected ASN.1 integer ID for R. + ErrSigInvalidRIntID = ErrorKind("ErrSigInvalidRIntID") + + // ErrSigZeroRLen is returned when a signature that should be a DER + // signature has an R length of zero. + ErrSigZeroRLen = ErrorKind("ErrSigZeroRLen") + + // ErrSigNegativeR is returned when a signature that should be a DER + // signature has a negative value for R. + ErrSigNegativeR = ErrorKind("ErrSigNegativeR") + + // ErrSigTooMuchRPadding is returned when a signature that should be a DER + // signature has too much padding for R. + ErrSigTooMuchRPadding = ErrorKind("ErrSigTooMuchRPadding") + + // ErrSigRIsZero is returned when a signature has R set to the value zero. + ErrSigRIsZero = ErrorKind("ErrSigRIsZero") + + // ErrSigRTooBig is returned when a signature has R with a value that is + // greater than or equal to the group order. + ErrSigRTooBig = ErrorKind("ErrSigRTooBig") + + // ErrSigInvalidSIntID is returned when a signature that should be a DER + // signature does not have the expected ASN.1 integer ID for S. + ErrSigInvalidSIntID = ErrorKind("ErrSigInvalidSIntID") + + // ErrSigZeroSLen is returned when a signature that should be a DER + // signature has an S length of zero. + ErrSigZeroSLen = ErrorKind("ErrSigZeroSLen") + + // ErrSigNegativeS is returned when a signature that should be a DER + // signature has a negative value for S. + ErrSigNegativeS = ErrorKind("ErrSigNegativeS") + + // ErrSigTooMuchSPadding is returned when a signature that should be a DER + // signature has too much padding for S. + ErrSigTooMuchSPadding = ErrorKind("ErrSigTooMuchSPadding") + + // ErrSigSIsZero is returned when a signature has S set to the value zero. + ErrSigSIsZero = ErrorKind("ErrSigSIsZero") + + // ErrSigSTooBig is returned when a signature has S with a value that is + // greater than or equal to the group order. + ErrSigSTooBig = ErrorKind("ErrSigSTooBig") +) + +// Error satisfies the error interface and prints human-readable errors. +func (e ErrorKind) Error() string { + return string(e) +} + +// Error identifies an error related to an ECDSA signature. It has full +// support for errors.Is and errors.As, so the caller can ascertain the +// specific reason for the error by checking the underlying error. +type Error struct { + Err error + Description string +} + +// Error satisfies the error interface and prints human-readable errors. +func (e Error) Error() string { + return e.Description +} + +// Unwrap returns the underlying wrapped error. +func (e Error) Unwrap() error { + return e.Err +} + +// signatureError creates an Error given a set of arguments. +func signatureError(kind ErrorKind, desc string) Error { + return Error{Err: kind, Description: desc} +} diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/ecdsa/signature.go b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/ecdsa/signature.go new file mode 100644 index 0000000..50f1721 --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/ecdsa/signature.go @@ -0,0 +1,925 @@ +// Copyright (c) 2013-2014 The btcsuite developers +// Copyright (c) 2015-2020 The Decred developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +package ecdsa + +import ( + "errors" + "fmt" + + "github.com/decred/dcrd/dcrec/secp256k1/v4" +) + +// References: +// [GECC]: Guide to Elliptic Curve Cryptography (Hankerson, Menezes, Vanstone) +// +// [ISO/IEC 8825-1]: Information technology — ASN.1 encoding rules: +// Specification of Basic Encoding Rules (BER), Canonical Encoding Rules +// (CER) and Distinguished Encoding Rules (DER) +// +// [SEC1]: Elliptic Curve Cryptography (May 31, 2009, Version 2.0) +// https://www.secg.org/sec1-v2.pdf + +var ( + // zero32 is an array of 32 bytes used for the purposes of zeroing and is + // defined here to avoid extra allocations. + zero32 = [32]byte{} + + // orderAsFieldVal is the order of the secp256k1 curve group stored as a + // field value. It is provided here to avoid the need to create it multiple + // times. + orderAsFieldVal = func() *secp256k1.FieldVal { + var f secp256k1.FieldVal + f.SetByteSlice(secp256k1.Params().N.Bytes()) + return &f + }() +) + +const ( + // asn1SequenceID is the ASN.1 identifier for a sequence and is used when + // parsing and serializing signatures encoded with the Distinguished + // Encoding Rules (DER) format per section 10 of [ISO/IEC 8825-1]. + asn1SequenceID = 0x30 + + // asn1IntegerID is the ASN.1 identifier for an integer and is used when + // parsing and serializing signatures encoded with the Distinguished + // Encoding Rules (DER) format per section 10 of [ISO/IEC 8825-1]. + asn1IntegerID = 0x02 +) + +// Signature is a type representing an ECDSA signature. +type Signature struct { + r secp256k1.ModNScalar + s secp256k1.ModNScalar +} + +// NewSignature instantiates a new signature given some r and s values. +func NewSignature(r, s *secp256k1.ModNScalar) *Signature { + return &Signature{*r, *s} +} + +// Serialize returns the ECDSA signature in the Distinguished Encoding Rules +// (DER) format per section 10 of [ISO/IEC 8825-1] and such that the S component +// of the signature is less than or equal to the half order of the group. +// +// Note that the serialized bytes returned do not include the appended hash type +// used in Decred signature scripts. +func (sig *Signature) Serialize() []byte { + // The format of a DER encoded signature is as follows: + // + // 0x30 0x02 0x02 + // - 0x30 is the ASN.1 identifier for a sequence. + // - Total length is 1 byte and specifies length of all remaining data. + // - 0x02 is the ASN.1 identifier that specifies an integer follows. + // - Length of R is 1 byte and specifies how many bytes R occupies. + // - R is the arbitrary length big-endian encoded number which + // represents the R value of the signature. DER encoding dictates + // that the value must be encoded using the minimum possible number + // of bytes. This implies the first byte can only be null if the + // highest bit of the next byte is set in order to prevent it from + // being interpreted as a negative number. + // - 0x02 is once again the ASN.1 integer identifier. + // - Length of S is 1 byte and specifies how many bytes S occupies. + // - S is the arbitrary length big-endian encoded number which + // represents the S value of the signature. The encoding rules are + // identical as those for R. + + // Ensure the S component of the signature is less than or equal to the half + // order of the group because both S and its negation are valid signatures + // modulo the order, so this forces a consistent choice to reduce signature + // malleability. + sigS := new(secp256k1.ModNScalar).Set(&sig.s) + if sigS.IsOverHalfOrder() { + sigS.Negate() + } + + // Serialize the R and S components of the signature into their fixed + // 32-byte big-endian encoding. Note that the extra leading zero byte is + // used to ensure it is canonical per DER and will be stripped if needed + // below. + var rBuf, sBuf [33]byte + sig.r.PutBytesUnchecked(rBuf[1:33]) + sigS.PutBytesUnchecked(sBuf[1:33]) + + // Ensure the encoded bytes for the R and S components are canonical per DER + // by trimming all leading zero bytes so long as the next byte does not have + // the high bit set and it's not the final byte. + canonR, canonS := rBuf[:], sBuf[:] + for len(canonR) > 1 && canonR[0] == 0x00 && canonR[1]&0x80 == 0 { + canonR = canonR[1:] + } + for len(canonS) > 1 && canonS[0] == 0x00 && canonS[1]&0x80 == 0 { + canonS = canonS[1:] + } + + // Total length of returned signature is 1 byte for each magic and length + // (6 total), plus lengths of R and S. + totalLen := 6 + len(canonR) + len(canonS) + b := make([]byte, 0, totalLen) + b = append(b, asn1SequenceID) + b = append(b, byte(totalLen-2)) + b = append(b, asn1IntegerID) + b = append(b, byte(len(canonR))) + b = append(b, canonR...) + b = append(b, asn1IntegerID) + b = append(b, byte(len(canonS))) + b = append(b, canonS...) + return b +} + +// zeroArray32 zeroes the provided 32-byte buffer. +func zeroArray32(b *[32]byte) { + copy(b[:], zero32[:]) +} + +// fieldToModNScalar converts a field value to scalar modulo the group order and +// returns the scalar along with either 1 if it was reduced (aka it overflowed) +// or 0 otherwise. +// +// Note that a bool is not used here because it is not possible in Go to convert +// from a bool to numeric value in constant time and many constant-time +// operations require a numeric value. +func fieldToModNScalar(v *secp256k1.FieldVal) (secp256k1.ModNScalar, uint32) { + var buf [32]byte + v.PutBytes(&buf) + var s secp256k1.ModNScalar + overflow := s.SetBytes(&buf) + zeroArray32(&buf) + return s, overflow +} + +// modNScalarToField converts a scalar modulo the group order to a field value. +func modNScalarToField(v *secp256k1.ModNScalar) secp256k1.FieldVal { + var buf [32]byte + v.PutBytes(&buf) + var fv secp256k1.FieldVal + fv.SetBytes(&buf) + return fv +} + +// Verify returns whether or not the signature is valid for the provided hash +// and secp256k1 public key. +func (sig *Signature) Verify(hash []byte, pubKey *secp256k1.PublicKey) bool { + // The algorithm for verifying an ECDSA signature is given as algorithm 4.30 + // in [GECC]. + // + // The following is a paraphrased version for reference: + // + // G = curve generator + // N = curve order + // Q = public key + // m = message + // R, S = signature + // + // 1. Fail if R and S are not in [1, N-1] + // 2. e = H(m) + // 3. w = S^-1 mod N + // 4. u1 = e * w mod N + // u2 = R * w mod N + // 5. X = u1G + u2Q + // 6. Fail if X is the point at infinity + // 7. x = X.x mod N (X.x is the x coordinate of X) + // 8. Verified if x == R + // + // However, since all group operations are done internally in Jacobian + // projective space, the algorithm is modified slightly here in order to + // avoid an expensive inversion back into affine coordinates at step 7. + // Credits to Greg Maxwell for originally suggesting this optimization. + // + // Ordinarily, step 7 involves converting the x coordinate to affine by + // calculating x = x / z^2 (mod P) and then calculating the remainder as + // x = x (mod N). Then step 8 compares it to R. + // + // Note that since R is the x coordinate mod N from a random point that was + // originally mod P, and the cofactor of the secp256k1 curve is 1, there are + // only two possible x coordinates that the original random point could have + // been to produce R: x, where x < N, and x+N, where x+N < P. + // + // This implies that the signature is valid if either: + // a) R == X.x / X.z^2 (mod P) + // => R * X.z^2 == X.x (mod P) + // --or-- + // b) R + N < P && R + N == X.x / X.z^2 (mod P) + // => R + N < P && (R + N) * X.z^2 == X.x (mod P) + // + // Therefore the following modified algorithm is used: + // + // 1. Fail if R and S are not in [1, N-1] + // 2. e = H(m) + // 3. w = S^-1 mod N + // 4. u1 = e * w mod N + // u2 = R * w mod N + // 5. X = u1G + u2Q + // 6. Fail if X is the point at infinity + // 7. z = (X.z)^2 mod P (X.z is the z coordinate of X) + // 8. Verified if R * z == X.x (mod P) + // 9. Fail if R + N >= P + // 10. Verified if (R + N) * z == X.x (mod P) + + // Step 1. + // + // Fail if R and S are not in [1, N-1]. + if sig.r.IsZero() || sig.s.IsZero() { + return false + } + + // Step 2. + // + // e = H(m) + var e secp256k1.ModNScalar + e.SetByteSlice(hash) + + // Step 3. + // + // w = S^-1 mod N + w := new(secp256k1.ModNScalar).InverseValNonConst(&sig.s) + + // Step 4. + // + // u1 = e * w mod N + // u2 = R * w mod N + u1 := new(secp256k1.ModNScalar).Mul2(&e, w) + u2 := new(secp256k1.ModNScalar).Mul2(&sig.r, w) + + // Step 5. + // + // X = u1G + u2Q + var X, Q, u1G, u2Q secp256k1.JacobianPoint + pubKey.AsJacobian(&Q) + secp256k1.ScalarBaseMultNonConst(u1, &u1G) + secp256k1.ScalarMultNonConst(u2, &Q, &u2Q) + secp256k1.AddNonConst(&u1G, &u2Q, &X) + + // Step 6. + // + // Fail if X is the point at infinity + if (X.X.IsZero() && X.Y.IsZero()) || X.Z.IsZero() { + return false + } + + // Step 7. + // + // z = (X.z)^2 mod P (X.z is the z coordinate of X) + z := new(secp256k1.FieldVal).SquareVal(&X.Z) + + // Step 8. + // + // Verified if R * z == X.x (mod P) + sigRModP := modNScalarToField(&sig.r) + result := new(secp256k1.FieldVal).Mul2(&sigRModP, z).Normalize() + if result.Equals(&X.X) { + return true + } + + // Step 9. + // + // Fail if R + N >= P + if sigRModP.IsGtOrEqPrimeMinusOrder() { + return false + } + + // Step 10. + // + // Verified if (R + N) * z == X.x (mod P) + sigRModP.Add(orderAsFieldVal) + result.Mul2(&sigRModP, z).Normalize() + return result.Equals(&X.X) +} + +// IsEqual compares this Signature instance to the one passed, returning true if +// both Signatures are equivalent. A signature is equivalent to another, if +// they both have the same scalar value for R and S. +func (sig *Signature) IsEqual(otherSig *Signature) bool { + return sig.r.Equals(&otherSig.r) && sig.s.Equals(&otherSig.s) +} + +// ParseDERSignature parses a signature in the Distinguished Encoding Rules +// (DER) format per section 10 of [ISO/IEC 8825-1] and enforces the following +// additional restrictions specific to secp256k1: +// +// - The R and S values must be in the valid range for secp256k1 scalars: +// - Negative values are rejected +// - Zero is rejected +// - Values greater than or equal to the secp256k1 group order are rejected +func ParseDERSignature(sig []byte) (*Signature, error) { + // The format of a DER encoded signature for secp256k1 is as follows: + // + // 0x30 0x02 0x02 + // - 0x30 is the ASN.1 identifier for a sequence + // - Total length is 1 byte and specifies length of all remaining data + // - 0x02 is the ASN.1 identifier that specifies an integer follows + // - Length of R is 1 byte and specifies how many bytes R occupies + // - R is the arbitrary length big-endian encoded number which + // represents the R value of the signature. DER encoding dictates + // that the value must be encoded using the minimum possible number + // of bytes. This implies the first byte can only be null if the + // highest bit of the next byte is set in order to prevent it from + // being interpreted as a negative number. + // - 0x02 is once again the ASN.1 integer identifier + // - Length of S is 1 byte and specifies how many bytes S occupies + // - S is the arbitrary length big-endian encoded number which + // represents the S value of the signature. The encoding rules are + // identical as those for R. + // + // NOTE: The DER specification supports specifying lengths that can occupy + // more than 1 byte, however, since this is specific to secp256k1 + // signatures, all lengths will be a single byte. + const ( + // minSigLen is the minimum length of a DER encoded signature and is + // when both R and S are 1 byte each. + // + // 0x30 + <1-byte> + 0x02 + 0x01 + + 0x2 + 0x01 + + minSigLen = 8 + + // maxSigLen is the maximum length of a DER encoded signature and is + // when both R and S are 33 bytes each. It is 33 bytes because a + // 256-bit integer requires 32 bytes and an additional leading null byte + // might be required if the high bit is set in the value. + // + // 0x30 + <1-byte> + 0x02 + 0x21 + <33 bytes> + 0x2 + 0x21 + <33 bytes> + maxSigLen = 72 + + // sequenceOffset is the byte offset within the signature of the + // expected ASN.1 sequence identifier. + sequenceOffset = 0 + + // dataLenOffset is the byte offset within the signature of the expected + // total length of all remaining data in the signature. + dataLenOffset = 1 + + // rTypeOffset is the byte offset within the signature of the ASN.1 + // identifier for R and is expected to indicate an ASN.1 integer. + rTypeOffset = 2 + + // rLenOffset is the byte offset within the signature of the length of + // R. + rLenOffset = 3 + + // rOffset is the byte offset within the signature of R. + rOffset = 4 + ) + + // The signature must adhere to the minimum and maximum allowed length. + sigLen := len(sig) + if sigLen < minSigLen { + str := fmt.Sprintf("malformed signature: too short: %d < %d", sigLen, + minSigLen) + return nil, signatureError(ErrSigTooShort, str) + } + if sigLen > maxSigLen { + str := fmt.Sprintf("malformed signature: too long: %d > %d", sigLen, + maxSigLen) + return nil, signatureError(ErrSigTooLong, str) + } + + // The signature must start with the ASN.1 sequence identifier. + if sig[sequenceOffset] != asn1SequenceID { + str := fmt.Sprintf("malformed signature: format has wrong type: %#x", + sig[sequenceOffset]) + return nil, signatureError(ErrSigInvalidSeqID, str) + } + + // The signature must indicate the correct amount of data for all elements + // related to R and S. + if int(sig[dataLenOffset]) != sigLen-2 { + str := fmt.Sprintf("malformed signature: bad length: %d != %d", + sig[dataLenOffset], sigLen-2) + return nil, signatureError(ErrSigInvalidDataLen, str) + } + + // Calculate the offsets of the elements related to S and ensure S is inside + // the signature. + // + // rLen specifies the length of the big-endian encoded number which + // represents the R value of the signature. + // + // sTypeOffset is the offset of the ASN.1 identifier for S and, like its R + // counterpart, is expected to indicate an ASN.1 integer. + // + // sLenOffset and sOffset are the byte offsets within the signature of the + // length of S and S itself, respectively. + rLen := int(sig[rLenOffset]) + sTypeOffset := rOffset + rLen + sLenOffset := sTypeOffset + 1 + if sTypeOffset >= sigLen { + str := "malformed signature: S type indicator missing" + return nil, signatureError(ErrSigMissingSTypeID, str) + } + if sLenOffset >= sigLen { + str := "malformed signature: S length missing" + return nil, signatureError(ErrSigMissingSLen, str) + } + + // The lengths of R and S must match the overall length of the signature. + // + // sLen specifies the length of the big-endian encoded number which + // represents the S value of the signature. + sOffset := sLenOffset + 1 + sLen := int(sig[sLenOffset]) + if sOffset+sLen != sigLen { + str := "malformed signature: invalid S length" + return nil, signatureError(ErrSigInvalidSLen, str) + } + + // R elements must be ASN.1 integers. + if sig[rTypeOffset] != asn1IntegerID { + str := fmt.Sprintf("malformed signature: R integer marker: %#x != %#x", + sig[rTypeOffset], asn1IntegerID) + return nil, signatureError(ErrSigInvalidRIntID, str) + } + + // Zero-length integers are not allowed for R. + if rLen == 0 { + str := "malformed signature: R length is zero" + return nil, signatureError(ErrSigZeroRLen, str) + } + + // R must not be negative. + if sig[rOffset]&0x80 != 0 { + str := "malformed signature: R is negative" + return nil, signatureError(ErrSigNegativeR, str) + } + + // Null bytes at the start of R are not allowed, unless R would otherwise be + // interpreted as a negative number. + if rLen > 1 && sig[rOffset] == 0x00 && sig[rOffset+1]&0x80 == 0 { + str := "malformed signature: R value has too much padding" + return nil, signatureError(ErrSigTooMuchRPadding, str) + } + + // S elements must be ASN.1 integers. + if sig[sTypeOffset] != asn1IntegerID { + str := fmt.Sprintf("malformed signature: S integer marker: %#x != %#x", + sig[sTypeOffset], asn1IntegerID) + return nil, signatureError(ErrSigInvalidSIntID, str) + } + + // Zero-length integers are not allowed for S. + if sLen == 0 { + str := "malformed signature: S length is zero" + return nil, signatureError(ErrSigZeroSLen, str) + } + + // S must not be negative. + if sig[sOffset]&0x80 != 0 { + str := "malformed signature: S is negative" + return nil, signatureError(ErrSigNegativeS, str) + } + + // Null bytes at the start of S are not allowed, unless S would otherwise be + // interpreted as a negative number. + if sLen > 1 && sig[sOffset] == 0x00 && sig[sOffset+1]&0x80 == 0 { + str := "malformed signature: S value has too much padding" + return nil, signatureError(ErrSigTooMuchSPadding, str) + } + + // The signature is validly encoded per DER at this point, however, enforce + // additional restrictions to ensure R and S are in the range [1, N-1] since + // valid ECDSA signatures are required to be in that range per spec. + // + // Also note that while the overflow checks are required to make use of the + // specialized mod N scalar type, rejecting zero here is not strictly + // required because it is also checked when verifying the signature, but + // there really isn't a good reason not to fail early here on signatures + // that do not conform to the ECDSA spec. + + // Strip leading zeroes from R. + rBytes := sig[rOffset : rOffset+rLen] + for len(rBytes) > 0 && rBytes[0] == 0x00 { + rBytes = rBytes[1:] + } + + // R must be in the range [1, N-1]. Notice the check for the maximum number + // of bytes is required because SetByteSlice truncates as noted in its + // comment so it could otherwise fail to detect the overflow. + var r secp256k1.ModNScalar + if len(rBytes) > 32 { + str := "invalid signature: R is larger than 256 bits" + return nil, signatureError(ErrSigRTooBig, str) + } + if overflow := r.SetByteSlice(rBytes); overflow { + str := "invalid signature: R >= group order" + return nil, signatureError(ErrSigRTooBig, str) + } + if r.IsZero() { + str := "invalid signature: R is 0" + return nil, signatureError(ErrSigRIsZero, str) + } + + // Strip leading zeroes from S. + sBytes := sig[sOffset : sOffset+sLen] + for len(sBytes) > 0 && sBytes[0] == 0x00 { + sBytes = sBytes[1:] + } + + // S must be in the range [1, N-1]. Notice the check for the maximum number + // of bytes is required because SetByteSlice truncates as noted in its + // comment so it could otherwise fail to detect the overflow. + var s secp256k1.ModNScalar + if len(sBytes) > 32 { + str := "invalid signature: S is larger than 256 bits" + return nil, signatureError(ErrSigSTooBig, str) + } + if overflow := s.SetByteSlice(sBytes); overflow { + str := "invalid signature: S >= group order" + return nil, signatureError(ErrSigSTooBig, str) + } + if s.IsZero() { + str := "invalid signature: S is 0" + return nil, signatureError(ErrSigSIsZero, str) + } + + // Create and return the signature. + return NewSignature(&r, &s), nil +} + +// signRFC6979 generates a deterministic ECDSA signature according to RFC 6979 +// and BIP 62 and returns it along with an additional public key recovery code +// for efficiently recovering the public key from the signature. +func signRFC6979(privKey *secp256k1.PrivateKey, hash []byte) (*Signature, byte) { + // The algorithm for producing an ECDSA signature is given as algorithm 4.29 + // in [GECC]. + // + // The following is a paraphrased version for reference: + // + // G = curve generator + // N = curve order + // d = private key + // m = message + // r, s = signature + // + // 1. Select random nonce k in [1, N-1] + // 2. Compute kG + // 3. r = kG.x mod N (kG.x is the x coordinate of the point kG) + // Repeat from step 1 if r = 0 + // 4. e = H(m) + // 5. s = k^-1(e + dr) mod N + // Repeat from step 1 if s = 0 + // 6. Return (r,s) + // + // This is slightly modified here to conform to RFC6979 and BIP 62 as + // follows: + // + // A. Instead of selecting a random nonce in step 1, use RFC6979 to generate + // a deterministic nonce in [1, N-1] parameterized by the private key, + // message being signed, and an iteration count for the repeat cases + // B. Negate s calculated in step 5 if it is > N/2 + // This is done because both s and its negation are valid signatures + // modulo the curve order N, so it forces a consistent choice to reduce + // signature malleability + + privKeyScalar := &privKey.Key + var privKeyBytes [32]byte + privKeyScalar.PutBytes(&privKeyBytes) + defer zeroArray32(&privKeyBytes) + for iteration := uint32(0); ; iteration++ { + // Step 1 with modification A. + // + // Generate a deterministic nonce in [1, N-1] parameterized by the + // private key, message being signed, and iteration count. + k := secp256k1.NonceRFC6979(privKeyBytes[:], hash, nil, nil, iteration) + + // Step 2. + // + // Compute kG + // + // Note that the point must be in affine coordinates. + var kG secp256k1.JacobianPoint + secp256k1.ScalarBaseMultNonConst(k, &kG) + kG.ToAffine() + + // Step 3. + // + // r = kG.x mod N + // Repeat from step 1 if r = 0 + r, overflow := fieldToModNScalar(&kG.X) + if r.IsZero() { + k.Zero() + continue + } + + // Since the secp256k1 curve has a cofactor of 1, when recovering a + // public key from an ECDSA signature over it, there are four possible + // candidates corresponding to the following cases: + // + // 1) The X coord of the random point is < N and its Y coord even + // 2) The X coord of the random point is < N and its Y coord is odd + // 3) The X coord of the random point is >= N and its Y coord is even + // 4) The X coord of the random point is >= N and its Y coord is odd + // + // Rather than forcing the recovery procedure to check all possible + // cases, this creates a recovery code that uniquely identifies which of + // the cases apply by making use of 2 bits. Bit 0 identifies the + // oddness case and Bit 1 identifies the overflow case (aka when the X + // coord >= N). + // + // It is also worth noting that making use of Hasse's theorem shows + // there are around log_2((p-n)/p) ~= -127.65 ~= 1 in 2^127 points where + // the X coordinate is >= N. It is not possible to calculate these + // points since that would require breaking the ECDLP, but, in practice + // this strongly implies with extremely high probability that there are + // only a few actual points for which this case is true. + pubKeyRecoveryCode := byte(overflow<<1) | byte(kG.Y.IsOddBit()) + + // Step 4. + // + // e = H(m) + // + // Note that this actually sets e = H(m) mod N which is correct since + // it is only used in step 5 which itself is mod N. + var e secp256k1.ModNScalar + e.SetByteSlice(hash) + + // Step 5 with modification B. + // + // s = k^-1(e + dr) mod N + // Repeat from step 1 if s = 0 + // s = -s if s > N/2 + kInv := new(secp256k1.ModNScalar).InverseValNonConst(k) + k.Zero() + s := new(secp256k1.ModNScalar).Mul2(privKeyScalar, &r).Add(&e).Mul(kInv) + if s.IsZero() { + continue + } + if s.IsOverHalfOrder() { + s.Negate() + + // Negating s corresponds to the random point that would have been + // generated by -k (mod N), which necessarily has the opposite + // oddness since N is prime, thus flip the pubkey recovery code + // oddness bit accordingly. + pubKeyRecoveryCode ^= 0x01 + } + + // Step 6. + // + // Return (r,s) + return NewSignature(&r, s), pubKeyRecoveryCode + } +} + +// Sign generates an ECDSA signature over the secp256k1 curve for the provided +// hash (which should be the result of hashing a larger message) using the given +// private key. The produced signature is deterministic (same message and same +// key yield the same signature) and canonical in accordance with RFC6979 and +// BIP0062. +func Sign(key *secp256k1.PrivateKey, hash []byte) *Signature { + signature, _ := signRFC6979(key, hash) + return signature +} + +const ( + // compactSigSize is the size of a compact signature. It consists of a + // compact signature recovery code byte followed by the R and S components + // serialized as 32-byte big-endian values. 1+32*2 = 65. + // for the R and S components. 1+32+32=65. + compactSigSize = 65 + + // compactSigMagicOffset is a value used when creating the compact signature + // recovery code inherited from Bitcoin and has no meaning, but has been + // retained for compatibility. For historical purposes, it was originally + // picked to avoid a binary representation that would allow compact + // signatures to be mistaken for other components. + compactSigMagicOffset = 27 + + // compactSigCompPubKey is a value used when creating the compact signature + // recovery code to indicate the original public key was compressed. + compactSigCompPubKey = 4 + + // pubKeyRecoveryCodeOddnessBit specifies the bit that indicates the oddess + // of the Y coordinate of the random point calculated when creating a + // signature. + pubKeyRecoveryCodeOddnessBit = 1 << 0 + + // pubKeyRecoveryCodeOverflowBit specifies the bit that indicates the X + // coordinate of the random point calculated when creating a signature was + // >= N, where N is the order of the group. + pubKeyRecoveryCodeOverflowBit = 1 << 1 +) + +// SignCompact produces a compact ECDSA signature over the secp256k1 curve for +// the provided hash (which should be the result of hashing a larger message) +// using the given private key. The isCompressedKey parameter specifies if the +// produced signature should reference a compressed public key or not. +// +// Compact signature format: +// <1-byte compact sig recovery code><32-byte R><32-byte S> +// +// The compact sig recovery code is the value 27 + public key recovery code + 4 +// if the compact signature was created with a compressed public key. +func SignCompact(key *secp256k1.PrivateKey, hash []byte, isCompressedKey bool) []byte { + // Create the signature and associated pubkey recovery code and calculate + // the compact signature recovery code. + sig, pubKeyRecoveryCode := signRFC6979(key, hash) + compactSigRecoveryCode := compactSigMagicOffset + pubKeyRecoveryCode + if isCompressedKey { + compactSigRecoveryCode += compactSigCompPubKey + } + + // Output <32-byte R><32-byte S>. + var b [compactSigSize]byte + b[0] = compactSigRecoveryCode + sig.r.PutBytesUnchecked(b[1:33]) + sig.s.PutBytesUnchecked(b[33:65]) + return b[:] +} + +// RecoverCompact attempts to recover the secp256k1 public key from the provided +// compact signature and message hash. It first verifies the signature, and, if +// the signature matches then the recovered public key will be returned as well +// as a boolean indicating whether or not the original key was compressed. +func RecoverCompact(signature, hash []byte) (*secp256k1.PublicKey, bool, error) { + // The following is very loosely based on the information and algorithm that + // describes recovering a public key from and ECDSA signature in section + // 4.1.6 of [SEC1]. + // + // Given the following parameters: + // + // G = curve generator + // N = group order + // P = field prime + // Q = public key + // m = message + // e = hash of the message + // r, s = signature + // X = random point used when creating signature whose x coordinate is r + // + // The equation to recover a public key candidate from an ECDSA signature + // is: + // Q = r^-1(sX - eG). + // + // This can be verified by plugging it in for Q in the sig verification + // equation: + // X = s^-1(eG + rQ) (mod N) + // => s^-1(eG + r(r^-1(sX - eG))) (mod N) + // => s^-1(eG + sX - eG) (mod N) + // => s^-1(sX) (mod N) + // => X (mod N) + // + // However, note that since r is the x coordinate mod N from a random point + // that was originally mod P, and the cofactor of the secp256k1 curve is 1, + // there are four possible points that the original random point could have + // been to produce r: (r,y), (r,-y), (r+N,y), and (r+N,-y). At least 2 of + // those points will successfully verify, and all 4 will successfully verify + // when the original x coordinate was in the range [N+1, P-1], but in any + // case, only one of them corresponds to the original private key used. + // + // The method described by section 4.1.6 of [SEC1] to determine which one is + // the correct one involves calculating each possibility as a candidate + // public key and comparing the candidate to the authentic public key. It + // also hints that is is possible to generate the signature in a such a + // way that only one of the candidate public keys is viable. + // + // A more efficient approach that is specific to the secp256k1 curve is used + // here instead which is to produce a "pubkey recovery code" when signing + // that uniquely identifies which of the 4 possibilities is correct for the + // original random point and using that to recover the pubkey directly as + // follows: + // + // 1. Fail if r and s are not in [1, N-1] + // 2. Convert r to integer mod P + // 3. If pubkey recovery code overflow bit is set: + // 3.1 Fail if r + N >= P + // 3.2 r = r + N (mod P) + // 4. y = +sqrt(r^3 + 7) (mod P) + // 4.1 Fail if y does not exist + // 4.2 y = -y if needed to match pubkey recovery code oddness bit + // 5. X = (r, y) + // 6. e = H(m) mod N + // 7. w = r^-1 mod N + // 8. u1 = -(e * w) mod N + // u2 = s * w mod N + // 9. Q = u1G + u2X + // 10. Fail if Q is the point at infinity + + // A compact signature consists of a recovery byte followed by the R and + // S components serialized as 32-byte big-endian values. + if len(signature) != compactSigSize { + return nil, false, errors.New("invalid compact signature size") + } + + // Parse and validate the compact signature recovery code. + const ( + minValidCode = compactSigMagicOffset + maxValidCode = compactSigMagicOffset + compactSigCompPubKey + 3 + ) + sigRecoveryCode := signature[0] + if sigRecoveryCode < minValidCode || sigRecoveryCode > maxValidCode { + return nil, false, errors.New("invalid compact signature recovery code") + } + sigRecoveryCode -= compactSigMagicOffset + wasCompressed := sigRecoveryCode&compactSigCompPubKey != 0 + pubKeyRecoveryCode := sigRecoveryCode & 3 + + // Step 1. + // + // Parse and validate the R and S signature components. + // + // Fail if r and s are not in [1, N-1]. + var r, s secp256k1.ModNScalar + if overflow := r.SetByteSlice(signature[1:33]); overflow { + return nil, false, errors.New("signature R is >= curve order") + } + if r.IsZero() { + return nil, false, errors.New("signature R is 0") + } + if overflow := s.SetByteSlice(signature[33:]); overflow { + return nil, false, errors.New("signature S is >= curve order") + } + if s.IsZero() { + return nil, false, errors.New("signature S is 0") + } + + // Step 2. + // + // Convert r to integer mod P. + fieldR := modNScalarToField(&r) + + // Step 3. + // + // If pubkey recovery code overflow bit is set: + if pubKeyRecoveryCode&pubKeyRecoveryCodeOverflowBit != 0 { + // Step 3.1. + // + // Fail if r + N >= P + // + // Either the signature or the recovery code must be invalid if the + // recovery code overflow bit is set and adding N to the R component + // would exceed the field prime since R originally came from the X + // coordinate of a random point on the curve. + if fieldR.IsGtOrEqPrimeMinusOrder() { + return nil, false, errors.New("signature R + N >= P") + } + + // Step 3.2. + // + // r = r + N (mod P) + fieldR.Add(orderAsFieldVal) + } + + // Step 4. + // + // y = +sqrt(r^3 + 7) (mod P) + // Fail if y does not exist. + // y = -y if needed to match pubkey recovery code oddness bit + // + // The signature must be invalid if the calculation fails because the X + // coord originally came from a random point on the curve which means there + // must be a Y coord that satisfies the equation for a valid signature. + oddY := pubKeyRecoveryCode&pubKeyRecoveryCodeOddnessBit != 0 + var y secp256k1.FieldVal + if valid := secp256k1.DecompressY(&fieldR, oddY, &y); !valid { + return nil, false, errors.New("signature is not for a valid curve point") + } + + // Step 5. + // + // X = (r, y) + var X secp256k1.JacobianPoint + X.X.Set(&fieldR) + X.Y.Set(&y) + X.Z.SetInt(1) + + // Step 6. + // + // e = H(m) mod N + var e secp256k1.ModNScalar + e.SetByteSlice(hash) + + // Step 7. + // + // w = r^-1 mod N + w := new(secp256k1.ModNScalar).InverseValNonConst(&r) + + // Step 8. + // + // u1 = -(e * w) mod N + // u2 = s * w mod N + u1 := new(secp256k1.ModNScalar).Mul2(&e, w).Negate() + u2 := new(secp256k1.ModNScalar).Mul2(&s, w) + + // Step 9. + // + // Q = u1G + u2X + var Q, u1G, u2X secp256k1.JacobianPoint + secp256k1.ScalarBaseMultNonConst(u1, &u1G) + secp256k1.ScalarMultNonConst(u2, &X, &u2X) + secp256k1.AddNonConst(&u1G, &u2X, &Q) + + // Step 10. + // + // Fail if Q is the point at infinity. + // + // Either the signature or the pubkey recovery code must be invalid if the + // recovered pubkey is the point at infinity. + if (Q.X.IsZero() && Q.Y.IsZero()) || Q.Z.IsZero() { + return nil, false, errors.New("recovered pubkey is the point at infinity") + } + + // Notice that the public key is in affine coordinates. + Q.ToAffine() + pubKey := secp256k1.NewPublicKey(&Q.X, &Q.Y) + return pubKey, wasCompressed, nil +} diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/ellipticadaptor.go b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/ellipticadaptor.go new file mode 100644 index 0000000..a271ff6 --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/ellipticadaptor.go @@ -0,0 +1,255 @@ +// Copyright 2020-2021 The Decred developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +package secp256k1 + +// References: +// [SECG]: Recommended Elliptic Curve Domain Parameters +// https://www.secg.org/sec2-v2.pdf +// +// [GECC]: Guide to Elliptic Curve Cryptography (Hankerson, Menezes, Vanstone) + +import ( + "crypto/ecdsa" + "crypto/elliptic" + "math/big" +) + +// CurveParams contains the parameters for the secp256k1 curve. +type CurveParams struct { + // P is the prime used in the secp256k1 field. + P *big.Int + + // N is the order of the secp256k1 curve group generated by the base point. + N *big.Int + + // Gx and Gy are the x and y coordinate of the base point, respectively. + Gx, Gy *big.Int + + // BitSize is the size of the underlying secp256k1 field in bits. + BitSize int + + // H is the cofactor of the secp256k1 curve. + H int + + // ByteSize is simply the bit size / 8 and is provided for convenience + // since it is calculated repeatedly. + ByteSize int +} + +// Curve parameters taken from [SECG] section 2.4.1. +var curveParams = CurveParams{ + P: fromHex("fffffffffffffffffffffffffffffffffffffffffffffffffffffffefffffc2f"), + N: fromHex("fffffffffffffffffffffffffffffffebaaedce6af48a03bbfd25e8cd0364141"), + Gx: fromHex("79be667ef9dcbbac55a06295ce870b07029bfcdb2dce28d959f2815b16f81798"), + Gy: fromHex("483ada7726a3c4655da4fbfc0e1108a8fd17b448a68554199c47d08ffb10d4b8"), + BitSize: 256, + H: 1, + ByteSize: 256 / 8, +} + +// Params returns the secp256k1 curve parameters for convenience. +func Params() *CurveParams { + return &curveParams +} + +// KoblitzCurve provides an implementation for secp256k1 that fits the ECC Curve +// interface from crypto/elliptic. +type KoblitzCurve struct { + *elliptic.CurveParams +} + +// bigAffineToJacobian takes an affine point (x, y) as big integers and converts +// it to Jacobian point with Z=1. +func bigAffineToJacobian(x, y *big.Int, result *JacobianPoint) { + result.X.SetByteSlice(x.Bytes()) + result.Y.SetByteSlice(y.Bytes()) + result.Z.SetInt(1) +} + +// jacobianToBigAffine takes a Jacobian point (x, y, z) as field values and +// converts it to an affine point as big integers. +func jacobianToBigAffine(point *JacobianPoint) (*big.Int, *big.Int) { + point.ToAffine() + + // Convert the field values for the now affine point to big.Ints. + x3, y3 := new(big.Int), new(big.Int) + x3.SetBytes(point.X.Bytes()[:]) + y3.SetBytes(point.Y.Bytes()[:]) + return x3, y3 +} + +// Params returns the parameters for the curve. +// +// This is part of the elliptic.Curve interface implementation. +func (curve *KoblitzCurve) Params() *elliptic.CurveParams { + return curve.CurveParams +} + +// IsOnCurve returns whether or not the affine point (x,y) is on the curve. +// +// This is part of the elliptic.Curve interface implementation. This function +// differs from the crypto/elliptic algorithm since a = 0 not -3. +func (curve *KoblitzCurve) IsOnCurve(x, y *big.Int) bool { + // Convert big ints to a Jacobian point for faster arithmetic. + var point JacobianPoint + bigAffineToJacobian(x, y, &point) + return isOnCurve(&point.X, &point.Y) +} + +// Add returns the sum of (x1,y1) and (x2,y2). +// +// This is part of the elliptic.Curve interface implementation. +func (curve *KoblitzCurve) Add(x1, y1, x2, y2 *big.Int) (*big.Int, *big.Int) { + // A point at infinity is the identity according to the group law for + // elliptic curve cryptography. Thus, ∞ + P = P and P + ∞ = P. + if x1.Sign() == 0 && y1.Sign() == 0 { + return x2, y2 + } + if x2.Sign() == 0 && y2.Sign() == 0 { + return x1, y1 + } + + // Convert the affine coordinates from big integers to Jacobian points, + // do the point addition in Jacobian projective space, and convert the + // Jacobian point back to affine big.Ints. + var p1, p2, result JacobianPoint + bigAffineToJacobian(x1, y1, &p1) + bigAffineToJacobian(x2, y2, &p2) + AddNonConst(&p1, &p2, &result) + return jacobianToBigAffine(&result) +} + +// Double returns 2*(x1,y1). +// +// This is part of the elliptic.Curve interface implementation. +func (curve *KoblitzCurve) Double(x1, y1 *big.Int) (*big.Int, *big.Int) { + if y1.Sign() == 0 { + return new(big.Int), new(big.Int) + } + + // Convert the affine coordinates from big integers to Jacobian points, + // do the point doubling in Jacobian projective space, and convert the + // Jacobian point back to affine big.Ints. + var point, result JacobianPoint + bigAffineToJacobian(x1, y1, &point) + DoubleNonConst(&point, &result) + return jacobianToBigAffine(&result) +} + +// moduloReduce reduces k from more than 32 bytes to 32 bytes and under. This +// is done by doing a simple modulo curve.N. We can do this since G^N = 1 and +// thus any other valid point on the elliptic curve has the same order. +func moduloReduce(k []byte) []byte { + // Since the order of G is curve.N, we can use a much smaller number by + // doing modulo curve.N + if len(k) > curveParams.ByteSize { + tmpK := new(big.Int).SetBytes(k) + tmpK.Mod(tmpK, curveParams.N) + return tmpK.Bytes() + } + + return k +} + +// ScalarMult returns k*(Bx, By) where k is a big endian integer. +// +// This is part of the elliptic.Curve interface implementation. +func (curve *KoblitzCurve) ScalarMult(Bx, By *big.Int, k []byte) (*big.Int, *big.Int) { + // Convert the affine coordinates from big integers to Jacobian points, + // do the multiplication in Jacobian projective space, and convert the + // Jacobian point back to affine big.Ints. + var kModN ModNScalar + kModN.SetByteSlice(moduloReduce(k)) + var point, result JacobianPoint + bigAffineToJacobian(Bx, By, &point) + ScalarMultNonConst(&kModN, &point, &result) + return jacobianToBigAffine(&result) +} + +// ScalarBaseMult returns k*G where G is the base point of the group and k is a +// big endian integer. +// +// This is part of the elliptic.Curve interface implementation. +func (curve *KoblitzCurve) ScalarBaseMult(k []byte) (*big.Int, *big.Int) { + // Perform the multiplication and convert the Jacobian point back to affine + // big.Ints. + var kModN ModNScalar + kModN.SetByteSlice(moduloReduce(k)) + var result JacobianPoint + ScalarBaseMultNonConst(&kModN, &result) + return jacobianToBigAffine(&result) +} + +// X returns the x coordinate of the public key. +func (p *PublicKey) X() *big.Int { + return new(big.Int).SetBytes(p.x.Bytes()[:]) +} + +// Y returns the y coordinate of the public key. +func (p *PublicKey) Y() *big.Int { + return new(big.Int).SetBytes(p.y.Bytes()[:]) +} + +// ToECDSA returns the public key as a *ecdsa.PublicKey. +func (p *PublicKey) ToECDSA() *ecdsa.PublicKey { + return &ecdsa.PublicKey{ + Curve: S256(), + X: p.X(), + Y: p.Y(), + } +} + +// ToECDSA returns the private key as a *ecdsa.PrivateKey. +func (p *PrivateKey) ToECDSA() *ecdsa.PrivateKey { + var privKeyBytes [PrivKeyBytesLen]byte + p.Key.PutBytes(&privKeyBytes) + var result JacobianPoint + ScalarBaseMultNonConst(&p.Key, &result) + x, y := jacobianToBigAffine(&result) + newPrivKey := &ecdsa.PrivateKey{ + PublicKey: ecdsa.PublicKey{ + Curve: S256(), + X: x, + Y: y, + }, + D: new(big.Int).SetBytes(privKeyBytes[:]), + } + zeroArray32(&privKeyBytes) + return newPrivKey +} + +// fromHex converts the passed hex string into a big integer pointer and will +// panic is there is an error. This is only provided for the hard-coded +// constants so errors in the source code can bet detected. It will only (and +// must only) be called for initialization purposes. +func fromHex(s string) *big.Int { + if s == "" { + return big.NewInt(0) + } + r, ok := new(big.Int).SetString(s, 16) + if !ok { + panic("invalid hex in source file: " + s) + } + return r +} + +// secp256k1 is a global instance of the KoblitzCurve implementation which in +// turn embeds and implements elliptic.CurveParams. +var secp256k1 = &KoblitzCurve{ + CurveParams: &elliptic.CurveParams{ + P: curveParams.P, + N: curveParams.N, + B: fromHex("0000000000000000000000000000000000000000000000000000000000000007"), + Gx: curveParams.Gx, + Gy: curveParams.Gy, + BitSize: curveParams.BitSize, + Name: "secp256k1", + }, +} + +// S256 returns a Curve which implements secp256k1. +func S256() *KoblitzCurve { + return secp256k1 +} diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/error.go b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/error.go new file mode 100644 index 0000000..ac8c451 --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/error.go @@ -0,0 +1,67 @@ +// Copyright (c) 2020 The Decred developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +package secp256k1 + +// ErrorKind identifies a kind of error. It has full support for errors.Is and +// errors.As, so the caller can directly check against an error kind when +// determining the reason for an error. +type ErrorKind string + +// These constants are used to identify a specific RuleError. +const ( + // ErrPubKeyInvalidLen indicates that the length of a serialized public + // key is not one of the allowed lengths. + ErrPubKeyInvalidLen = ErrorKind("ErrPubKeyInvalidLen") + + // ErrPubKeyInvalidFormat indicates an attempt was made to parse a public + // key that does not specify one of the supported formats. + ErrPubKeyInvalidFormat = ErrorKind("ErrPubKeyInvalidFormat") + + // ErrPubKeyXTooBig indicates that the x coordinate for a public key + // is greater than or equal to the prime of the field underlying the group. + ErrPubKeyXTooBig = ErrorKind("ErrPubKeyXTooBig") + + // ErrPubKeyYTooBig indicates that the y coordinate for a public key is + // greater than or equal to the prime of the field underlying the group. + ErrPubKeyYTooBig = ErrorKind("ErrPubKeyYTooBig") + + // ErrPubKeyNotOnCurve indicates that a public key is not a point on the + // secp256k1 curve. + ErrPubKeyNotOnCurve = ErrorKind("ErrPubKeyNotOnCurve") + + // ErrPubKeyMismatchedOddness indicates that a hybrid public key specified + // an oddness of the y coordinate that does not match the actual oddness of + // the provided y coordinate. + ErrPubKeyMismatchedOddness = ErrorKind("ErrPubKeyMismatchedOddness") +) + +// Error satisfies the error interface and prints human-readable errors. +func (e ErrorKind) Error() string { + return string(e) +} + +// Error identifies an error related to public key cryptography using a +// sec256k1 curve. It has full support for errors.Is and errors.As, so the +// caller can ascertain the specific reason for the error by checking +// the underlying error. +type Error struct { + Err error + Description string +} + +// Error satisfies the error interface and prints human-readable errors. +func (e Error) Error() string { + return e.Description +} + +// Unwrap returns the underlying wrapped error. +func (e Error) Unwrap() error { + return e.Err +} + +// makeError creates an Error given a set of arguments. +func makeError(kind ErrorKind, desc string) Error { + return Error{Err: kind, Description: desc} +} diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/field.go b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/field.go new file mode 100644 index 0000000..43e27a9 --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/field.go @@ -0,0 +1,1680 @@ +// Copyright (c) 2013-2014 The btcsuite developers +// Copyright (c) 2015-2021 The Decred developers +// Copyright (c) 2013-2021 Dave Collins +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +package secp256k1 + +// References: +// [HAC]: Handbook of Applied Cryptography Menezes, van Oorschot, Vanstone. +// http://cacr.uwaterloo.ca/hac/ + +// All elliptic curve operations for secp256k1 are done in a finite field +// characterized by a 256-bit prime. Given this precision is larger than the +// biggest available native type, obviously some form of bignum math is needed. +// This package implements specialized fixed-precision field arithmetic rather +// than relying on an arbitrary-precision arithmetic package such as math/big +// for dealing with the field math since the size is known. As a result, rather +// large performance gains are achieved by taking advantage of many +// optimizations not available to arbitrary-precision arithmetic and generic +// modular arithmetic algorithms. +// +// There are various ways to internally represent each finite field element. +// For example, the most obvious representation would be to use an array of 4 +// uint64s (64 bits * 4 = 256 bits). However, that representation suffers from +// a couple of issues. First, there is no native Go type large enough to handle +// the intermediate results while adding or multiplying two 64-bit numbers, and +// second there is no space left for overflows when performing the intermediate +// arithmetic between each array element which would lead to expensive carry +// propagation. +// +// Given the above, this implementation represents the field elements as +// 10 uint32s with each word (array entry) treated as base 2^26. This was +// chosen for the following reasons: +// 1) Most systems at the current time are 64-bit (or at least have 64-bit +// registers available for specialized purposes such as MMX) so the +// intermediate results can typically be done using a native register (and +// using uint64s to avoid the need for additional half-word arithmetic) +// 2) In order to allow addition of the internal words without having to +// propagate the carry, the max normalized value for each register must +// be less than the number of bits available in the register +// 3) Since we're dealing with 32-bit values, 64-bits of overflow is a +// reasonable choice for #2 +// 4) Given the need for 256-bits of precision and the properties stated in #1, +// #2, and #3, the representation which best accommodates this is 10 uint32s +// with base 2^26 (26 bits * 10 = 260 bits, so the final word only needs 22 +// bits) which leaves the desired 64 bits (32 * 10 = 320, 320 - 256 = 64) for +// overflow +// +// Since it is so important that the field arithmetic is extremely fast for high +// performance crypto, this type does not perform any validation where it +// ordinarily would. See the documentation for FieldVal for more details. + +import ( + "encoding/hex" +) + +// Constants used to make the code more readable. +const ( + twoBitsMask = 0x3 + fourBitsMask = 0xf + sixBitsMask = 0x3f + eightBitsMask = 0xff +) + +// Constants related to the field representation. +const ( + // fieldWords is the number of words used to internally represent the + // 256-bit value. + fieldWords = 10 + + // fieldBase is the exponent used to form the numeric base of each word. + // 2^(fieldBase*i) where i is the word position. + fieldBase = 26 + + // fieldBaseMask is the mask for the bits in each word needed to + // represent the numeric base of each word (except the most significant + // word). + fieldBaseMask = (1 << fieldBase) - 1 + + // fieldMSBBits is the number of bits in the most significant word used + // to represent the value. + fieldMSBBits = 256 - (fieldBase * (fieldWords - 1)) + + // fieldMSBMask is the mask for the bits in the most significant word + // needed to represent the value. + fieldMSBMask = (1 << fieldMSBBits) - 1 + + // These fields provide convenient access to each of the words of the + // secp256k1 prime in the internal field representation to improve code + // readability. + fieldPrimeWordZero = 0x03fffc2f + fieldPrimeWordOne = 0x03ffffbf + fieldPrimeWordTwo = 0x03ffffff + fieldPrimeWordThree = 0x03ffffff + fieldPrimeWordFour = 0x03ffffff + fieldPrimeWordFive = 0x03ffffff + fieldPrimeWordSix = 0x03ffffff + fieldPrimeWordSeven = 0x03ffffff + fieldPrimeWordEight = 0x03ffffff + fieldPrimeWordNine = 0x003fffff +) + +// FieldVal implements optimized fixed-precision arithmetic over the +// secp256k1 finite field. This means all arithmetic is performed modulo +// 0xfffffffffffffffffffffffffffffffffffffffffffffffffffffffefffffc2f. +// +// WARNING: Since it is so important for the field arithmetic to be extremely +// fast for high performance crypto, this type does not perform any validation +// of documented preconditions where it ordinarily would. As a result, it is +// IMPERATIVE for callers to understand some key concepts that are described +// below and ensure the methods are called with the necessary preconditions that +// each method is documented with. For example, some methods only give the +// correct result if the field value is normalized and others require the field +// values involved to have a maximum magnitude and THERE ARE NO EXPLICIT CHECKS +// TO ENSURE THOSE PRECONDITIONS ARE SATISFIED. This does, unfortunately, make +// the type more difficult to use correctly and while I typically prefer to +// ensure all state and input is valid for most code, this is a bit of an +// exception because those extra checks really add up in what ends up being +// critical hot paths. +// +// The first key concept when working with this type is normalization. In order +// to avoid the need to propagate a ton of carries, the internal representation +// provides additional overflow bits for each word of the overall 256-bit value. +// This means that there are multiple internal representations for the same +// value and, as a result, any methods that rely on comparison of the value, +// such as equality and oddness determination, require the caller to provide a +// normalized value. +// +// The second key concept when working with this type is magnitude. As +// previously mentioned, the internal representation provides additional +// overflow bits which means that the more math operations that are performed on +// the field value between normalizations, the more those overflow bits +// accumulate. The magnitude is effectively that maximum possible number of +// those overflow bits that could possibly be required as a result of a given +// operation. Since there are only a limited number of overflow bits available, +// this implies that the max possible magnitude MUST be tracked by the caller +// and the caller MUST normalize the field value if a given operation would +// cause the magnitude of the result to exceed the max allowed value. +// +// IMPORTANT: The max allowed magnitude of a field value is 64. +type FieldVal struct { + // Each 256-bit value is represented as 10 32-bit integers in base 2^26. + // This provides 6 bits of overflow in each word (10 bits in the most + // significant word) for a total of 64 bits of overflow (9*6 + 10 = 64). It + // only implements the arithmetic needed for elliptic curve operations. + // + // The following depicts the internal representation: + // ----------------------------------------------------------------- + // | n[9] | n[8] | ... | n[0] | + // | 32 bits available | 32 bits available | ... | 32 bits available | + // | 22 bits for value | 26 bits for value | ... | 26 bits for value | + // | 10 bits overflow | 6 bits overflow | ... | 6 bits overflow | + // | Mult: 2^(26*9) | Mult: 2^(26*8) | ... | Mult: 2^(26*0) | + // ----------------------------------------------------------------- + // + // For example, consider the number 2^49 + 1. It would be represented as: + // n[0] = 1 + // n[1] = 2^23 + // n[2..9] = 0 + // + // The full 256-bit value is then calculated by looping i from 9..0 and + // doing sum(n[i] * 2^(26i)) like so: + // n[9] * 2^(26*9) = 0 * 2^234 = 0 + // n[8] * 2^(26*8) = 0 * 2^208 = 0 + // ... + // n[1] * 2^(26*1) = 2^23 * 2^26 = 2^49 + // n[0] * 2^(26*0) = 1 * 2^0 = 1 + // Sum: 0 + 0 + ... + 2^49 + 1 = 2^49 + 1 + n [10]uint32 +} + +// String returns the field value as a normalized human-readable hex string. +// +// Preconditions: None +// Output Normalized: Field is not modified -- same as input value +// Output Max Magnitude: Field is not modified -- same as input value +func (f FieldVal) String() string { + // f is a copy, so it's safe to normalize it without mutating the original. + f.Normalize() + return hex.EncodeToString(f.Bytes()[:]) +} + +// Zero sets the field value to zero in constant time. A newly created field +// value is already set to zero. This function can be useful to clear an +// existing field value for reuse. +// +// Preconditions: None +// Output Normalized: Yes +// Output Max Magnitude: 1 +func (f *FieldVal) Zero() { + f.n[0] = 0 + f.n[1] = 0 + f.n[2] = 0 + f.n[3] = 0 + f.n[4] = 0 + f.n[5] = 0 + f.n[6] = 0 + f.n[7] = 0 + f.n[8] = 0 + f.n[9] = 0 +} + +// Set sets the field value equal to the passed value in constant time. The +// normalization and magnitude of the two fields will be identical. +// +// The field value is returned to support chaining. This enables syntax like: +// f := new(FieldVal).Set(f2).Add(1) so that f = f2 + 1 where f2 is not +// modified. +// +// Preconditions: None +// Output Normalized: Same as input value +// Output Max Magnitude: Same as input value +func (f *FieldVal) Set(val *FieldVal) *FieldVal { + *f = *val + return f +} + +// SetInt sets the field value to the passed integer in constant time. This is +// a convenience function since it is fairly common to perform some arithmetic +// with small native integers. +// +// The field value is returned to support chaining. This enables syntax such +// as f := new(FieldVal).SetInt(2).Mul(f2) so that f = 2 * f2. +// +// Preconditions: None +// Output Normalized: Yes +// Output Max Magnitude: 1 +func (f *FieldVal) SetInt(ui uint16) *FieldVal { + f.Zero() + f.n[0] = uint32(ui) + return f +} + +// SetBytes packs the passed 32-byte big-endian value into the internal field +// value representation in constant time. SetBytes interprets the provided +// array as a 256-bit big-endian unsigned integer, packs it into the internal +// field value representation, and returns either 1 if it is greater than or +// equal to the field prime (aka it overflowed) or 0 otherwise in constant time. +// +// Note that a bool is not used here because it is not possible in Go to convert +// from a bool to numeric value in constant time and many constant-time +// operations require a numeric value. +// +// Preconditions: None +// Output Normalized: Yes if no overflow, no otherwise +// Output Max Magnitude: 1 +func (f *FieldVal) SetBytes(b *[32]byte) uint32 { + // Pack the 256 total bits across the 10 uint32 words with a max of + // 26-bits per word. This could be done with a couple of for loops, + // but this unrolled version is significantly faster. Benchmarks show + // this is about 34 times faster than the variant which uses loops. + f.n[0] = uint32(b[31]) | uint32(b[30])<<8 | uint32(b[29])<<16 | + (uint32(b[28])&twoBitsMask)<<24 + f.n[1] = uint32(b[28])>>2 | uint32(b[27])<<6 | uint32(b[26])<<14 | + (uint32(b[25])&fourBitsMask)<<22 + f.n[2] = uint32(b[25])>>4 | uint32(b[24])<<4 | uint32(b[23])<<12 | + (uint32(b[22])&sixBitsMask)<<20 + f.n[3] = uint32(b[22])>>6 | uint32(b[21])<<2 | uint32(b[20])<<10 | + uint32(b[19])<<18 + f.n[4] = uint32(b[18]) | uint32(b[17])<<8 | uint32(b[16])<<16 | + (uint32(b[15])&twoBitsMask)<<24 + f.n[5] = uint32(b[15])>>2 | uint32(b[14])<<6 | uint32(b[13])<<14 | + (uint32(b[12])&fourBitsMask)<<22 + f.n[6] = uint32(b[12])>>4 | uint32(b[11])<<4 | uint32(b[10])<<12 | + (uint32(b[9])&sixBitsMask)<<20 + f.n[7] = uint32(b[9])>>6 | uint32(b[8])<<2 | uint32(b[7])<<10 | + uint32(b[6])<<18 + f.n[8] = uint32(b[5]) | uint32(b[4])<<8 | uint32(b[3])<<16 | + (uint32(b[2])&twoBitsMask)<<24 + f.n[9] = uint32(b[2])>>2 | uint32(b[1])<<6 | uint32(b[0])<<14 + + // The intuition here is that the field value is greater than the prime if + // one of the higher individual words is greater than corresponding word of + // the prime and all higher words in the field value are equal to their + // corresponding word of the prime. Since this type is modulo the prime, + // being equal is also an overflow back to 0. + // + // Note that because the input is 32 bytes and it was just packed into the + // field representation, the only words that can possibly be greater are + // zero and one, because ceil(log_2(2^256 - 1 - P)) = 33 bits max and the + // internal field representation encodes 26 bits with each word. + // + // Thus, there is no need to test if the upper words of the field value + // exceeds them, hence, only equality is checked for them. + highWordsEq := constantTimeEq(f.n[9], fieldPrimeWordNine) + highWordsEq &= constantTimeEq(f.n[8], fieldPrimeWordEight) + highWordsEq &= constantTimeEq(f.n[7], fieldPrimeWordSeven) + highWordsEq &= constantTimeEq(f.n[6], fieldPrimeWordSix) + highWordsEq &= constantTimeEq(f.n[5], fieldPrimeWordFive) + highWordsEq &= constantTimeEq(f.n[4], fieldPrimeWordFour) + highWordsEq &= constantTimeEq(f.n[3], fieldPrimeWordThree) + highWordsEq &= constantTimeEq(f.n[2], fieldPrimeWordTwo) + overflow := highWordsEq & constantTimeGreater(f.n[1], fieldPrimeWordOne) + highWordsEq &= constantTimeEq(f.n[1], fieldPrimeWordOne) + overflow |= highWordsEq & constantTimeGreaterOrEq(f.n[0], fieldPrimeWordZero) + + return overflow +} + +// SetByteSlice interprets the provided slice as a 256-bit big-endian unsigned +// integer (meaning it is truncated to the first 32 bytes), packs it into the +// internal field value representation, and returns whether or not the resulting +// truncated 256-bit integer is greater than or equal to the field prime (aka it +// overflowed) in constant time. +// +// Note that since passing a slice with more than 32 bytes is truncated, it is +// possible that the truncated value is less than the field prime and hence it +// will not be reported as having overflowed in that case. It is up to the +// caller to decide whether it needs to provide numbers of the appropriate size +// or it if is acceptable to use this function with the described truncation and +// overflow behavior. +// +// Preconditions: None +// Output Normalized: Yes if no overflow, no otherwise +// Output Max Magnitude: 1 +func (f *FieldVal) SetByteSlice(b []byte) bool { + var b32 [32]byte + b = b[:constantTimeMin(uint32(len(b)), 32)] + copy(b32[:], b32[:32-len(b)]) + copy(b32[32-len(b):], b) + result := f.SetBytes(&b32) + zeroArray32(&b32) + return result != 0 +} + +// Normalize normalizes the internal field words into the desired range and +// performs fast modular reduction over the secp256k1 prime by making use of the +// special form of the prime in constant time. +// +// Preconditions: None +// Output Normalized: Yes +// Output Max Magnitude: 1 +func (f *FieldVal) Normalize() *FieldVal { + // The field representation leaves 6 bits of overflow in each word so + // intermediate calculations can be performed without needing to + // propagate the carry to each higher word during the calculations. In + // order to normalize, we need to "compact" the full 256-bit value to + // the right while propagating any carries through to the high order + // word. + // + // Since this field is doing arithmetic modulo the secp256k1 prime, we + // also need to perform modular reduction over the prime. + // + // Per [HAC] section 14.3.4: Reduction method of moduli of special form, + // when the modulus is of the special form m = b^t - c, highly efficient + // reduction can be achieved. + // + // The secp256k1 prime is equivalent to 2^256 - 4294968273, so it fits + // this criteria. + // + // 4294968273 in field representation (base 2^26) is: + // n[0] = 977 + // n[1] = 64 + // That is to say (2^26 * 64) + 977 = 4294968273 + // + // The algorithm presented in the referenced section typically repeats + // until the quotient is zero. However, due to our field representation + // we already know to within one reduction how many times we would need + // to repeat as it's the uppermost bits of the high order word. Thus we + // can simply multiply the magnitude by the field representation of the + // prime and do a single iteration. After this step there might be an + // additional carry to bit 256 (bit 22 of the high order word). + t9 := f.n[9] + m := t9 >> fieldMSBBits + t9 = t9 & fieldMSBMask + t0 := f.n[0] + m*977 + t1 := (t0 >> fieldBase) + f.n[1] + (m << 6) + t0 = t0 & fieldBaseMask + t2 := (t1 >> fieldBase) + f.n[2] + t1 = t1 & fieldBaseMask + t3 := (t2 >> fieldBase) + f.n[3] + t2 = t2 & fieldBaseMask + t4 := (t3 >> fieldBase) + f.n[4] + t3 = t3 & fieldBaseMask + t5 := (t4 >> fieldBase) + f.n[5] + t4 = t4 & fieldBaseMask + t6 := (t5 >> fieldBase) + f.n[6] + t5 = t5 & fieldBaseMask + t7 := (t6 >> fieldBase) + f.n[7] + t6 = t6 & fieldBaseMask + t8 := (t7 >> fieldBase) + f.n[8] + t7 = t7 & fieldBaseMask + t9 = (t8 >> fieldBase) + t9 + t8 = t8 & fieldBaseMask + + // At this point, the magnitude is guaranteed to be one, however, the + // value could still be greater than the prime if there was either a + // carry through to bit 256 (bit 22 of the higher order word) or the + // value is greater than or equal to the field characteristic. The + // following determines if either or these conditions are true and does + // the final reduction in constant time. + // + // Also note that 'm' will be zero when neither of the aforementioned + // conditions are true and the value will not be changed when 'm' is zero. + m = constantTimeEq(t9, fieldMSBMask) + m &= constantTimeEq(t8&t7&t6&t5&t4&t3&t2, fieldBaseMask) + m &= constantTimeGreater(t1+64+((t0+977)>>fieldBase), fieldBaseMask) + m |= t9 >> fieldMSBBits + t0 = t0 + m*977 + t1 = (t0 >> fieldBase) + t1 + (m << 6) + t0 = t0 & fieldBaseMask + t2 = (t1 >> fieldBase) + t2 + t1 = t1 & fieldBaseMask + t3 = (t2 >> fieldBase) + t3 + t2 = t2 & fieldBaseMask + t4 = (t3 >> fieldBase) + t4 + t3 = t3 & fieldBaseMask + t5 = (t4 >> fieldBase) + t5 + t4 = t4 & fieldBaseMask + t6 = (t5 >> fieldBase) + t6 + t5 = t5 & fieldBaseMask + t7 = (t6 >> fieldBase) + t7 + t6 = t6 & fieldBaseMask + t8 = (t7 >> fieldBase) + t8 + t7 = t7 & fieldBaseMask + t9 = (t8 >> fieldBase) + t9 + t8 = t8 & fieldBaseMask + t9 = t9 & fieldMSBMask // Remove potential multiple of 2^256. + + // Finally, set the normalized and reduced words. + f.n[0] = t0 + f.n[1] = t1 + f.n[2] = t2 + f.n[3] = t3 + f.n[4] = t4 + f.n[5] = t5 + f.n[6] = t6 + f.n[7] = t7 + f.n[8] = t8 + f.n[9] = t9 + return f +} + +// PutBytesUnchecked unpacks the field value to a 32-byte big-endian value +// directly into the passed byte slice in constant time. The target slice must +// must have at least 32 bytes available or it will panic. +// +// There is a similar function, PutBytes, which unpacks the field value into a +// 32-byte array directly. This version is provided since it can be useful +// to write directly into part of a larger buffer without needing a separate +// allocation. +// +// Preconditions: +// - The field value MUST be normalized +// - The target slice MUST have at least 32 bytes available +func (f *FieldVal) PutBytesUnchecked(b []byte) { + // Unpack the 256 total bits from the 10 uint32 words with a max of + // 26-bits per word. This could be done with a couple of for loops, + // but this unrolled version is a bit faster. Benchmarks show this is + // about 10 times faster than the variant which uses loops. + b[31] = byte(f.n[0] & eightBitsMask) + b[30] = byte((f.n[0] >> 8) & eightBitsMask) + b[29] = byte((f.n[0] >> 16) & eightBitsMask) + b[28] = byte((f.n[0]>>24)&twoBitsMask | (f.n[1]&sixBitsMask)<<2) + b[27] = byte((f.n[1] >> 6) & eightBitsMask) + b[26] = byte((f.n[1] >> 14) & eightBitsMask) + b[25] = byte((f.n[1]>>22)&fourBitsMask | (f.n[2]&fourBitsMask)<<4) + b[24] = byte((f.n[2] >> 4) & eightBitsMask) + b[23] = byte((f.n[2] >> 12) & eightBitsMask) + b[22] = byte((f.n[2]>>20)&sixBitsMask | (f.n[3]&twoBitsMask)<<6) + b[21] = byte((f.n[3] >> 2) & eightBitsMask) + b[20] = byte((f.n[3] >> 10) & eightBitsMask) + b[19] = byte((f.n[3] >> 18) & eightBitsMask) + b[18] = byte(f.n[4] & eightBitsMask) + b[17] = byte((f.n[4] >> 8) & eightBitsMask) + b[16] = byte((f.n[4] >> 16) & eightBitsMask) + b[15] = byte((f.n[4]>>24)&twoBitsMask | (f.n[5]&sixBitsMask)<<2) + b[14] = byte((f.n[5] >> 6) & eightBitsMask) + b[13] = byte((f.n[5] >> 14) & eightBitsMask) + b[12] = byte((f.n[5]>>22)&fourBitsMask | (f.n[6]&fourBitsMask)<<4) + b[11] = byte((f.n[6] >> 4) & eightBitsMask) + b[10] = byte((f.n[6] >> 12) & eightBitsMask) + b[9] = byte((f.n[6]>>20)&sixBitsMask | (f.n[7]&twoBitsMask)<<6) + b[8] = byte((f.n[7] >> 2) & eightBitsMask) + b[7] = byte((f.n[7] >> 10) & eightBitsMask) + b[6] = byte((f.n[7] >> 18) & eightBitsMask) + b[5] = byte(f.n[8] & eightBitsMask) + b[4] = byte((f.n[8] >> 8) & eightBitsMask) + b[3] = byte((f.n[8] >> 16) & eightBitsMask) + b[2] = byte((f.n[8]>>24)&twoBitsMask | (f.n[9]&sixBitsMask)<<2) + b[1] = byte((f.n[9] >> 6) & eightBitsMask) + b[0] = byte((f.n[9] >> 14) & eightBitsMask) +} + +// PutBytes unpacks the field value to a 32-byte big-endian value using the +// passed byte array in constant time. +// +// There is a similar function, PutBytesUnchecked, which unpacks the field value +// into a slice that must have at least 32 bytes available. This version is +// provided since it can be useful to write directly into an array that is type +// checked. +// +// Alternatively, there is also Bytes, which unpacks the field value into a new +// array and returns that which can sometimes be more ergonomic in applications +// that aren't concerned about an additional copy. +// +// Preconditions: +// - The field value MUST be normalized +func (f *FieldVal) PutBytes(b *[32]byte) { + f.PutBytesUnchecked(b[:]) +} + +// Bytes unpacks the field value to a 32-byte big-endian value in constant time. +// +// See PutBytes and PutBytesUnchecked for variants that allow an array or slice +// to be passed which can be useful to cut down on the number of allocations by +// allowing the caller to reuse a buffer or write directly into part of a larger +// buffer. +// +// Preconditions: +// - The field value MUST be normalized +func (f *FieldVal) Bytes() *[32]byte { + b := new([32]byte) + f.PutBytesUnchecked(b[:]) + return b +} + +// IsZeroBit returns 1 when the field value is equal to zero or 0 otherwise in +// constant time. +// +// Note that a bool is not used here because it is not possible in Go to convert +// from a bool to numeric value in constant time and many constant-time +// operations require a numeric value. See IsZero for the version that returns +// a bool. +// +// Preconditions: +// - The field value MUST be normalized +func (f *FieldVal) IsZeroBit() uint32 { + // The value can only be zero if no bits are set in any of the words. + // This is a constant time implementation. + bits := f.n[0] | f.n[1] | f.n[2] | f.n[3] | f.n[4] | + f.n[5] | f.n[6] | f.n[7] | f.n[8] | f.n[9] + + return constantTimeEq(bits, 0) +} + +// IsZero returns whether or not the field value is equal to zero in constant +// time. +// +// Preconditions: +// - The field value MUST be normalized +func (f *FieldVal) IsZero() bool { + // The value can only be zero if no bits are set in any of the words. + // This is a constant time implementation. + bits := f.n[0] | f.n[1] | f.n[2] | f.n[3] | f.n[4] | + f.n[5] | f.n[6] | f.n[7] | f.n[8] | f.n[9] + + return bits == 0 +} + +// IsOneBit returns 1 when the field value is equal to one or 0 otherwise in +// constant time. +// +// Note that a bool is not used here because it is not possible in Go to convert +// from a bool to numeric value in constant time and many constant-time +// operations require a numeric value. See IsOne for the version that returns a +// bool. +// +// Preconditions: +// - The field value MUST be normalized +func (f *FieldVal) IsOneBit() uint32 { + // The value can only be one if the single lowest significant bit is set in + // the first word and no other bits are set in any of the other words. + // This is a constant time implementation. + bits := (f.n[0] ^ 1) | f.n[1] | f.n[2] | f.n[3] | f.n[4] | f.n[5] | + f.n[6] | f.n[7] | f.n[8] | f.n[9] + + return constantTimeEq(bits, 0) +} + +// IsOne returns whether or not the field value is equal to one in constant +// time. +// +// Preconditions: +// - The field value MUST be normalized +func (f *FieldVal) IsOne() bool { + // The value can only be one if the single lowest significant bit is set in + // the first word and no other bits are set in any of the other words. + // This is a constant time implementation. + bits := (f.n[0] ^ 1) | f.n[1] | f.n[2] | f.n[3] | f.n[4] | f.n[5] | + f.n[6] | f.n[7] | f.n[8] | f.n[9] + + return bits == 0 +} + +// IsOddBit returns 1 when the field value is an odd number or 0 otherwise in +// constant time. +// +// Note that a bool is not used here because it is not possible in Go to convert +// from a bool to numeric value in constant time and many constant-time +// operations require a numeric value. See IsOdd for the version that returns a +// bool. +// +// Preconditions: +// - The field value MUST be normalized +func (f *FieldVal) IsOddBit() uint32 { + // Only odd numbers have the bottom bit set. + return f.n[0] & 1 +} + +// IsOdd returns whether or not the field value is an odd number in constant +// time. +// +// Preconditions: +// - The field value MUST be normalized +func (f *FieldVal) IsOdd() bool { + // Only odd numbers have the bottom bit set. + return f.n[0]&1 == 1 +} + +// Equals returns whether or not the two field values are the same in constant +// time. +// +// Preconditions: +// - Both field values being compared MUST be normalized +func (f *FieldVal) Equals(val *FieldVal) bool { + // Xor only sets bits when they are different, so the two field values + // can only be the same if no bits are set after xoring each word. + // This is a constant time implementation. + bits := (f.n[0] ^ val.n[0]) | (f.n[1] ^ val.n[1]) | (f.n[2] ^ val.n[2]) | + (f.n[3] ^ val.n[3]) | (f.n[4] ^ val.n[4]) | (f.n[5] ^ val.n[5]) | + (f.n[6] ^ val.n[6]) | (f.n[7] ^ val.n[7]) | (f.n[8] ^ val.n[8]) | + (f.n[9] ^ val.n[9]) + + return bits == 0 +} + +// NegateVal negates the passed value and stores the result in f in constant +// time. The caller must provide the magnitude of the passed value for a +// correct result. +// +// The field value is returned to support chaining. This enables syntax like: +// f.NegateVal(f2).AddInt(1) so that f = -f2 + 1. +// +// Preconditions: +// - The max magnitude MUST be 63 +// Output Normalized: No +// Output Max Magnitude: Input magnitude + 1 +func (f *FieldVal) NegateVal(val *FieldVal, magnitude uint32) *FieldVal { + // Negation in the field is just the prime minus the value. However, + // in order to allow negation against a field value without having to + // normalize/reduce it first, multiply by the magnitude (that is how + // "far" away it is from the normalized value) to adjust. Also, since + // negating a value pushes it one more order of magnitude away from the + // normalized range, add 1 to compensate. + // + // For some intuition here, imagine you're performing mod 12 arithmetic + // (picture a clock) and you are negating the number 7. So you start at + // 12 (which is of course 0 under mod 12) and count backwards (left on + // the clock) 7 times to arrive at 5. Notice this is just 12-7 = 5. + // Now, assume you're starting with 19, which is a number that is + // already larger than the modulus and congruent to 7 (mod 12). When a + // value is already in the desired range, its magnitude is 1. Since 19 + // is an additional "step", its magnitude (mod 12) is 2. Since any + // multiple of the modulus is congruent to zero (mod m), the answer can + // be shortcut by simply multiplying the magnitude by the modulus and + // subtracting. Keeping with the example, this would be (2*12)-19 = 5. + f.n[0] = (magnitude+1)*fieldPrimeWordZero - val.n[0] + f.n[1] = (magnitude+1)*fieldPrimeWordOne - val.n[1] + f.n[2] = (magnitude+1)*fieldBaseMask - val.n[2] + f.n[3] = (magnitude+1)*fieldBaseMask - val.n[3] + f.n[4] = (magnitude+1)*fieldBaseMask - val.n[4] + f.n[5] = (magnitude+1)*fieldBaseMask - val.n[5] + f.n[6] = (magnitude+1)*fieldBaseMask - val.n[6] + f.n[7] = (magnitude+1)*fieldBaseMask - val.n[7] + f.n[8] = (magnitude+1)*fieldBaseMask - val.n[8] + f.n[9] = (magnitude+1)*fieldMSBMask - val.n[9] + + return f +} + +// Negate negates the field value in constant time. The existing field value is +// modified. The caller must provide the magnitude of the field value for a +// correct result. +// +// The field value is returned to support chaining. This enables syntax like: +// f.Negate().AddInt(1) so that f = -f + 1. +// +// Preconditions: +// - The max magnitude MUST be 63 +// Output Normalized: No +// Output Max Magnitude: Input magnitude + 1 +func (f *FieldVal) Negate(magnitude uint32) *FieldVal { + return f.NegateVal(f, magnitude) +} + +// AddInt adds the passed integer to the existing field value and stores the +// result in f in constant time. This is a convenience function since it is +// fairly common to perform some arithmetic with small native integers. +// +// The field value is returned to support chaining. This enables syntax like: +// f.AddInt(1).Add(f2) so that f = f + 1 + f2. +// +// Preconditions: +// - The field value MUST have a max magnitude of 63 +// Output Normalized: No +// Output Max Magnitude: Existing field magnitude + 1 +func (f *FieldVal) AddInt(ui uint16) *FieldVal { + // Since the field representation intentionally provides overflow bits, + // it's ok to use carryless addition as the carry bit is safely part of + // the word and will be normalized out. + f.n[0] += uint32(ui) + + return f +} + +// Add adds the passed value to the existing field value and stores the result +// in f in constant time. +// +// The field value is returned to support chaining. This enables syntax like: +// f.Add(f2).AddInt(1) so that f = f + f2 + 1. +// +// Preconditions: +// - The sum of the magnitudes of the two field values MUST be a max of 64 +// Output Normalized: No +// Output Max Magnitude: Sum of the magnitude of the two individual field values +func (f *FieldVal) Add(val *FieldVal) *FieldVal { + // Since the field representation intentionally provides overflow bits, + // it's ok to use carryless addition as the carry bit is safely part of + // each word and will be normalized out. This could obviously be done + // in a loop, but the unrolled version is faster. + f.n[0] += val.n[0] + f.n[1] += val.n[1] + f.n[2] += val.n[2] + f.n[3] += val.n[3] + f.n[4] += val.n[4] + f.n[5] += val.n[5] + f.n[6] += val.n[6] + f.n[7] += val.n[7] + f.n[8] += val.n[8] + f.n[9] += val.n[9] + + return f +} + +// Add2 adds the passed two field values together and stores the result in f in +// constant time. +// +// The field value is returned to support chaining. This enables syntax like: +// f3.Add2(f, f2).AddInt(1) so that f3 = f + f2 + 1. +// +// Preconditions: +// - The sum of the magnitudes of the two field values MUST be a max of 64 +// Output Normalized: No +// Output Max Magnitude: Sum of the magnitude of the two field values +func (f *FieldVal) Add2(val *FieldVal, val2 *FieldVal) *FieldVal { + // Since the field representation intentionally provides overflow bits, + // it's ok to use carryless addition as the carry bit is safely part of + // each word and will be normalized out. This could obviously be done + // in a loop, but the unrolled version is faster. + f.n[0] = val.n[0] + val2.n[0] + f.n[1] = val.n[1] + val2.n[1] + f.n[2] = val.n[2] + val2.n[2] + f.n[3] = val.n[3] + val2.n[3] + f.n[4] = val.n[4] + val2.n[4] + f.n[5] = val.n[5] + val2.n[5] + f.n[6] = val.n[6] + val2.n[6] + f.n[7] = val.n[7] + val2.n[7] + f.n[8] = val.n[8] + val2.n[8] + f.n[9] = val.n[9] + val2.n[9] + + return f +} + +// MulInt multiplies the field value by the passed int and stores the result in +// f in constant time. Note that this function can overflow if multiplying the +// value by any of the individual words exceeds a max uint32. Therefore it is +// important that the caller ensures no overflows will occur before using this +// function. +// +// The field value is returned to support chaining. This enables syntax like: +// f.MulInt(2).Add(f2) so that f = 2 * f + f2. +// +// Preconditions: +// - The field value magnitude multiplied by given val MUST be a max of 64 +// Output Normalized: No +// Output Max Magnitude: Existing field magnitude times the provided integer val +func (f *FieldVal) MulInt(val uint8) *FieldVal { + // Since each word of the field representation can hold up to + // 32 - fieldBase extra bits which will be normalized out, it's safe + // to multiply each word without using a larger type or carry + // propagation so long as the values won't overflow a uint32. This + // could obviously be done in a loop, but the unrolled version is + // faster. + ui := uint32(val) + f.n[0] *= ui + f.n[1] *= ui + f.n[2] *= ui + f.n[3] *= ui + f.n[4] *= ui + f.n[5] *= ui + f.n[6] *= ui + f.n[7] *= ui + f.n[8] *= ui + f.n[9] *= ui + + return f +} + +// Mul multiplies the passed value to the existing field value and stores the +// result in f in constant time. Note that this function can overflow if +// multiplying any of the individual words exceeds a max uint32. In practice, +// this means the magnitude of either value involved in the multiplication must +// be a max of 8. +// +// The field value is returned to support chaining. This enables syntax like: +// f.Mul(f2).AddInt(1) so that f = (f * f2) + 1. +// +// Preconditions: +// - Both field values MUST have a max magnitude of 8 +// Output Normalized: No +// Output Max Magnitude: 1 +func (f *FieldVal) Mul(val *FieldVal) *FieldVal { + return f.Mul2(f, val) +} + +// Mul2 multiplies the passed two field values together and stores the result +// result in f in constant time. Note that this function can overflow if +// multiplying any of the individual words exceeds a max uint32. In practice, +// this means the magnitude of either value involved in the multiplication must +// be a max of 8. +// +// The field value is returned to support chaining. This enables syntax like: +// f3.Mul2(f, f2).AddInt(1) so that f3 = (f * f2) + 1. +// +// Preconditions: +// - Both input field values MUST have a max magnitude of 8 +// Output Normalized: No +// Output Max Magnitude: 1 +func (f *FieldVal) Mul2(val *FieldVal, val2 *FieldVal) *FieldVal { + // This could be done with a couple of for loops and an array to store + // the intermediate terms, but this unrolled version is significantly + // faster. + + // Terms for 2^(fieldBase*0). + m := uint64(val.n[0]) * uint64(val2.n[0]) + t0 := m & fieldBaseMask + + // Terms for 2^(fieldBase*1). + m = (m >> fieldBase) + + uint64(val.n[0])*uint64(val2.n[1]) + + uint64(val.n[1])*uint64(val2.n[0]) + t1 := m & fieldBaseMask + + // Terms for 2^(fieldBase*2). + m = (m >> fieldBase) + + uint64(val.n[0])*uint64(val2.n[2]) + + uint64(val.n[1])*uint64(val2.n[1]) + + uint64(val.n[2])*uint64(val2.n[0]) + t2 := m & fieldBaseMask + + // Terms for 2^(fieldBase*3). + m = (m >> fieldBase) + + uint64(val.n[0])*uint64(val2.n[3]) + + uint64(val.n[1])*uint64(val2.n[2]) + + uint64(val.n[2])*uint64(val2.n[1]) + + uint64(val.n[3])*uint64(val2.n[0]) + t3 := m & fieldBaseMask + + // Terms for 2^(fieldBase*4). + m = (m >> fieldBase) + + uint64(val.n[0])*uint64(val2.n[4]) + + uint64(val.n[1])*uint64(val2.n[3]) + + uint64(val.n[2])*uint64(val2.n[2]) + + uint64(val.n[3])*uint64(val2.n[1]) + + uint64(val.n[4])*uint64(val2.n[0]) + t4 := m & fieldBaseMask + + // Terms for 2^(fieldBase*5). + m = (m >> fieldBase) + + uint64(val.n[0])*uint64(val2.n[5]) + + uint64(val.n[1])*uint64(val2.n[4]) + + uint64(val.n[2])*uint64(val2.n[3]) + + uint64(val.n[3])*uint64(val2.n[2]) + + uint64(val.n[4])*uint64(val2.n[1]) + + uint64(val.n[5])*uint64(val2.n[0]) + t5 := m & fieldBaseMask + + // Terms for 2^(fieldBase*6). + m = (m >> fieldBase) + + uint64(val.n[0])*uint64(val2.n[6]) + + uint64(val.n[1])*uint64(val2.n[5]) + + uint64(val.n[2])*uint64(val2.n[4]) + + uint64(val.n[3])*uint64(val2.n[3]) + + uint64(val.n[4])*uint64(val2.n[2]) + + uint64(val.n[5])*uint64(val2.n[1]) + + uint64(val.n[6])*uint64(val2.n[0]) + t6 := m & fieldBaseMask + + // Terms for 2^(fieldBase*7). + m = (m >> fieldBase) + + uint64(val.n[0])*uint64(val2.n[7]) + + uint64(val.n[1])*uint64(val2.n[6]) + + uint64(val.n[2])*uint64(val2.n[5]) + + uint64(val.n[3])*uint64(val2.n[4]) + + uint64(val.n[4])*uint64(val2.n[3]) + + uint64(val.n[5])*uint64(val2.n[2]) + + uint64(val.n[6])*uint64(val2.n[1]) + + uint64(val.n[7])*uint64(val2.n[0]) + t7 := m & fieldBaseMask + + // Terms for 2^(fieldBase*8). + m = (m >> fieldBase) + + uint64(val.n[0])*uint64(val2.n[8]) + + uint64(val.n[1])*uint64(val2.n[7]) + + uint64(val.n[2])*uint64(val2.n[6]) + + uint64(val.n[3])*uint64(val2.n[5]) + + uint64(val.n[4])*uint64(val2.n[4]) + + uint64(val.n[5])*uint64(val2.n[3]) + + uint64(val.n[6])*uint64(val2.n[2]) + + uint64(val.n[7])*uint64(val2.n[1]) + + uint64(val.n[8])*uint64(val2.n[0]) + t8 := m & fieldBaseMask + + // Terms for 2^(fieldBase*9). + m = (m >> fieldBase) + + uint64(val.n[0])*uint64(val2.n[9]) + + uint64(val.n[1])*uint64(val2.n[8]) + + uint64(val.n[2])*uint64(val2.n[7]) + + uint64(val.n[3])*uint64(val2.n[6]) + + uint64(val.n[4])*uint64(val2.n[5]) + + uint64(val.n[5])*uint64(val2.n[4]) + + uint64(val.n[6])*uint64(val2.n[3]) + + uint64(val.n[7])*uint64(val2.n[2]) + + uint64(val.n[8])*uint64(val2.n[1]) + + uint64(val.n[9])*uint64(val2.n[0]) + t9 := m & fieldBaseMask + + // Terms for 2^(fieldBase*10). + m = (m >> fieldBase) + + uint64(val.n[1])*uint64(val2.n[9]) + + uint64(val.n[2])*uint64(val2.n[8]) + + uint64(val.n[3])*uint64(val2.n[7]) + + uint64(val.n[4])*uint64(val2.n[6]) + + uint64(val.n[5])*uint64(val2.n[5]) + + uint64(val.n[6])*uint64(val2.n[4]) + + uint64(val.n[7])*uint64(val2.n[3]) + + uint64(val.n[8])*uint64(val2.n[2]) + + uint64(val.n[9])*uint64(val2.n[1]) + t10 := m & fieldBaseMask + + // Terms for 2^(fieldBase*11). + m = (m >> fieldBase) + + uint64(val.n[2])*uint64(val2.n[9]) + + uint64(val.n[3])*uint64(val2.n[8]) + + uint64(val.n[4])*uint64(val2.n[7]) + + uint64(val.n[5])*uint64(val2.n[6]) + + uint64(val.n[6])*uint64(val2.n[5]) + + uint64(val.n[7])*uint64(val2.n[4]) + + uint64(val.n[8])*uint64(val2.n[3]) + + uint64(val.n[9])*uint64(val2.n[2]) + t11 := m & fieldBaseMask + + // Terms for 2^(fieldBase*12). + m = (m >> fieldBase) + + uint64(val.n[3])*uint64(val2.n[9]) + + uint64(val.n[4])*uint64(val2.n[8]) + + uint64(val.n[5])*uint64(val2.n[7]) + + uint64(val.n[6])*uint64(val2.n[6]) + + uint64(val.n[7])*uint64(val2.n[5]) + + uint64(val.n[8])*uint64(val2.n[4]) + + uint64(val.n[9])*uint64(val2.n[3]) + t12 := m & fieldBaseMask + + // Terms for 2^(fieldBase*13). + m = (m >> fieldBase) + + uint64(val.n[4])*uint64(val2.n[9]) + + uint64(val.n[5])*uint64(val2.n[8]) + + uint64(val.n[6])*uint64(val2.n[7]) + + uint64(val.n[7])*uint64(val2.n[6]) + + uint64(val.n[8])*uint64(val2.n[5]) + + uint64(val.n[9])*uint64(val2.n[4]) + t13 := m & fieldBaseMask + + // Terms for 2^(fieldBase*14). + m = (m >> fieldBase) + + uint64(val.n[5])*uint64(val2.n[9]) + + uint64(val.n[6])*uint64(val2.n[8]) + + uint64(val.n[7])*uint64(val2.n[7]) + + uint64(val.n[8])*uint64(val2.n[6]) + + uint64(val.n[9])*uint64(val2.n[5]) + t14 := m & fieldBaseMask + + // Terms for 2^(fieldBase*15). + m = (m >> fieldBase) + + uint64(val.n[6])*uint64(val2.n[9]) + + uint64(val.n[7])*uint64(val2.n[8]) + + uint64(val.n[8])*uint64(val2.n[7]) + + uint64(val.n[9])*uint64(val2.n[6]) + t15 := m & fieldBaseMask + + // Terms for 2^(fieldBase*16). + m = (m >> fieldBase) + + uint64(val.n[7])*uint64(val2.n[9]) + + uint64(val.n[8])*uint64(val2.n[8]) + + uint64(val.n[9])*uint64(val2.n[7]) + t16 := m & fieldBaseMask + + // Terms for 2^(fieldBase*17). + m = (m >> fieldBase) + + uint64(val.n[8])*uint64(val2.n[9]) + + uint64(val.n[9])*uint64(val2.n[8]) + t17 := m & fieldBaseMask + + // Terms for 2^(fieldBase*18). + m = (m >> fieldBase) + uint64(val.n[9])*uint64(val2.n[9]) + t18 := m & fieldBaseMask + + // What's left is for 2^(fieldBase*19). + t19 := m >> fieldBase + + // At this point, all of the terms are grouped into their respective + // base. + // + // Per [HAC] section 14.3.4: Reduction method of moduli of special form, + // when the modulus is of the special form m = b^t - c, highly efficient + // reduction can be achieved per the provided algorithm. + // + // The secp256k1 prime is equivalent to 2^256 - 4294968273, so it fits + // this criteria. + // + // 4294968273 in field representation (base 2^26) is: + // n[0] = 977 + // n[1] = 64 + // That is to say (2^26 * 64) + 977 = 4294968273 + // + // Since each word is in base 26, the upper terms (t10 and up) start + // at 260 bits (versus the final desired range of 256 bits), so the + // field representation of 'c' from above needs to be adjusted for the + // extra 4 bits by multiplying it by 2^4 = 16. 4294968273 * 16 = + // 68719492368. Thus, the adjusted field representation of 'c' is: + // n[0] = 977 * 16 = 15632 + // n[1] = 64 * 16 = 1024 + // That is to say (2^26 * 1024) + 15632 = 68719492368 + // + // To reduce the final term, t19, the entire 'c' value is needed instead + // of only n[0] because there are no more terms left to handle n[1]. + // This means there might be some magnitude left in the upper bits that + // is handled below. + m = t0 + t10*15632 + t0 = m & fieldBaseMask + m = (m >> fieldBase) + t1 + t10*1024 + t11*15632 + t1 = m & fieldBaseMask + m = (m >> fieldBase) + t2 + t11*1024 + t12*15632 + t2 = m & fieldBaseMask + m = (m >> fieldBase) + t3 + t12*1024 + t13*15632 + t3 = m & fieldBaseMask + m = (m >> fieldBase) + t4 + t13*1024 + t14*15632 + t4 = m & fieldBaseMask + m = (m >> fieldBase) + t5 + t14*1024 + t15*15632 + t5 = m & fieldBaseMask + m = (m >> fieldBase) + t6 + t15*1024 + t16*15632 + t6 = m & fieldBaseMask + m = (m >> fieldBase) + t7 + t16*1024 + t17*15632 + t7 = m & fieldBaseMask + m = (m >> fieldBase) + t8 + t17*1024 + t18*15632 + t8 = m & fieldBaseMask + m = (m >> fieldBase) + t9 + t18*1024 + t19*68719492368 + t9 = m & fieldMSBMask + m = m >> fieldMSBBits + + // At this point, if the magnitude is greater than 0, the overall value + // is greater than the max possible 256-bit value. In particular, it is + // "how many times larger" than the max value it is. + // + // The algorithm presented in [HAC] section 14.3.4 repeats until the + // quotient is zero. However, due to the above, we already know at + // least how many times we would need to repeat as it's the value + // currently in m. Thus we can simply multiply the magnitude by the + // field representation of the prime and do a single iteration. Notice + // that nothing will be changed when the magnitude is zero, so we could + // skip this in that case, however always running regardless allows it + // to run in constant time. The final result will be in the range + // 0 <= result <= prime + (2^64 - c), so it is guaranteed to have a + // magnitude of 1, but it is denormalized. + d := t0 + m*977 + f.n[0] = uint32(d & fieldBaseMask) + d = (d >> fieldBase) + t1 + m*64 + f.n[1] = uint32(d & fieldBaseMask) + f.n[2] = uint32((d >> fieldBase) + t2) + f.n[3] = uint32(t3) + f.n[4] = uint32(t4) + f.n[5] = uint32(t5) + f.n[6] = uint32(t6) + f.n[7] = uint32(t7) + f.n[8] = uint32(t8) + f.n[9] = uint32(t9) + + return f +} + +// SquareRootVal either calculates the square root of the passed value when it +// exists or the square root of the negation of the value when it does not exist +// and stores the result in f in constant time. The return flag is true when +// the calculated square root is for the passed value itself and false when it +// is for its negation. +// +// Note that this function can overflow if multiplying any of the individual +// words exceeds a max uint32. In practice, this means the magnitude of the +// field must be a max of 8 to prevent overflow. The magnitude of the result +// will be 1. +// +// Preconditions: +// - The input field value MUST have a max magnitude of 8 +// Output Normalized: No +// Output Max Magnitude: 1 +func (f *FieldVal) SquareRootVal(val *FieldVal) bool { + // This uses the Tonelli-Shanks method for calculating the square root of + // the value when it exists. The key principles of the method follow. + // + // Fermat's little theorem states that for a nonzero number 'a' and prime + // 'p', a^(p-1) ≡ 1 (mod p). + // + // Further, Euler's criterion states that an integer 'a' has a square root + // (aka is a quadratic residue) modulo a prime if a^((p-1)/2) ≡ 1 (mod p) + // and, conversely, when it does NOT have a square root (aka 'a' is a + // non-residue) a^((p-1)/2) ≡ -1 (mod p). + // + // This can be seen by considering that Fermat's little theorem can be + // written as (a^((p-1)/2) - 1)(a^((p-1)/2) + 1) ≡ 0 (mod p). Therefore, + // one of the two factors must be 0. Then, when a ≡ x^2 (aka 'a' is a + // quadratic residue), (x^2)^((p-1)/2) ≡ x^(p-1) ≡ 1 (mod p) which implies + // the first factor must be zero. Finally, per Lagrange's theorem, the + // non-residues are the only remaining possible solutions and thus must make + // the second factor zero to satisfy Fermat's little theorem implying that + // a^((p-1)/2) ≡ -1 (mod p) for that case. + // + // The Tonelli-Shanks method uses these facts along with factoring out + // powers of two to solve a congruence that results in either the solution + // when the square root exists or the square root of the negation of the + // value when it does not. In the case of primes that are ≡ 3 (mod 4), the + // possible solutions are r = ±a^((p+1)/4) (mod p). Therefore, either r^2 ≡ + // a (mod p) is true in which case ±r are the two solutions, or r^2 ≡ -a + // (mod p) in which case 'a' is a non-residue and there are no solutions. + // + // The secp256k1 prime is ≡ 3 (mod 4), so this result applies. + // + // In other words, calculate a^((p+1)/4) and then square it and check it + // against the original value to determine if it is actually the square + // root. + // + // In order to efficiently compute a^((p+1)/4), (p+1)/4 needs to be split + // into a sequence of squares and multiplications that minimizes the number + // of multiplications needed (since they are more costly than squarings). + // + // The secp256k1 prime + 1 / 4 is 2^254 - 2^30 - 244. In binary, that is: + // + // 00111111 11111111 11111111 11111111 + // 11111111 11111111 11111111 11111111 + // 11111111 11111111 11111111 11111111 + // 11111111 11111111 11111111 11111111 + // 11111111 11111111 11111111 11111111 + // 11111111 11111111 11111111 11111111 + // 11111111 11111111 11111111 11111111 + // 10111111 11111111 11111111 00001100 + // + // Notice that can be broken up into three windows of consecutive 1s (in + // order of least to most signifcant) as: + // + // 6-bit window with two bits set (bits 4, 5, 6, 7 unset) + // 23-bit window with 22 bits set (bit 30 unset) + // 223-bit window with all 223 bits set + // + // Thus, the groups of 1 bits in each window forms the set: + // S = {2, 22, 223}. + // + // The strategy is to calculate a^(2^n - 1) for each grouping via an + // addition chain with a sliding window. + // + // The addition chain used is (credits to Peter Dettman): + // (0,0),(1,0),(2,2),(3,2),(4,1),(5,5),(6,6),(7,7),(8,8),(9,7),(10,2) + // => 2^1 2^[2] 2^3 2^6 2^9 2^11 2^[22] 2^44 2^88 2^176 2^220 2^[223] + // + // This has a cost of 254 field squarings and 13 field multiplications. + var a, a2, a3, a6, a9, a11, a22, a44, a88, a176, a220, a223 FieldVal + a.Set(val) + a2.SquareVal(&a).Mul(&a) // a2 = a^(2^2 - 1) + a3.SquareVal(&a2).Mul(&a) // a3 = a^(2^3 - 1) + a6.SquareVal(&a3).Square().Square() // a6 = a^(2^6 - 2^3) + a6.Mul(&a3) // a6 = a^(2^6 - 1) + a9.SquareVal(&a6).Square().Square() // a9 = a^(2^9 - 2^3) + a9.Mul(&a3) // a9 = a^(2^9 - 1) + a11.SquareVal(&a9).Square() // a11 = a^(2^11 - 2^2) + a11.Mul(&a2) // a11 = a^(2^11 - 1) + a22.SquareVal(&a11).Square().Square().Square().Square() // a22 = a^(2^16 - 2^5) + a22.Square().Square().Square().Square().Square() // a22 = a^(2^21 - 2^10) + a22.Square() // a22 = a^(2^22 - 2^11) + a22.Mul(&a11) // a22 = a^(2^22 - 1) + a44.SquareVal(&a22).Square().Square().Square().Square() // a44 = a^(2^27 - 2^5) + a44.Square().Square().Square().Square().Square() // a44 = a^(2^32 - 2^10) + a44.Square().Square().Square().Square().Square() // a44 = a^(2^37 - 2^15) + a44.Square().Square().Square().Square().Square() // a44 = a^(2^42 - 2^20) + a44.Square().Square() // a44 = a^(2^44 - 2^22) + a44.Mul(&a22) // a44 = a^(2^44 - 1) + a88.SquareVal(&a44).Square().Square().Square().Square() // a88 = a^(2^49 - 2^5) + a88.Square().Square().Square().Square().Square() // a88 = a^(2^54 - 2^10) + a88.Square().Square().Square().Square().Square() // a88 = a^(2^59 - 2^15) + a88.Square().Square().Square().Square().Square() // a88 = a^(2^64 - 2^20) + a88.Square().Square().Square().Square().Square() // a88 = a^(2^69 - 2^25) + a88.Square().Square().Square().Square().Square() // a88 = a^(2^74 - 2^30) + a88.Square().Square().Square().Square().Square() // a88 = a^(2^79 - 2^35) + a88.Square().Square().Square().Square().Square() // a88 = a^(2^84 - 2^40) + a88.Square().Square().Square().Square() // a88 = a^(2^88 - 2^44) + a88.Mul(&a44) // a88 = a^(2^88 - 1) + a176.SquareVal(&a88).Square().Square().Square().Square() // a176 = a^(2^93 - 2^5) + a176.Square().Square().Square().Square().Square() // a176 = a^(2^98 - 2^10) + a176.Square().Square().Square().Square().Square() // a176 = a^(2^103 - 2^15) + a176.Square().Square().Square().Square().Square() // a176 = a^(2^108 - 2^20) + a176.Square().Square().Square().Square().Square() // a176 = a^(2^113 - 2^25) + a176.Square().Square().Square().Square().Square() // a176 = a^(2^118 - 2^30) + a176.Square().Square().Square().Square().Square() // a176 = a^(2^123 - 2^35) + a176.Square().Square().Square().Square().Square() // a176 = a^(2^128 - 2^40) + a176.Square().Square().Square().Square().Square() // a176 = a^(2^133 - 2^45) + a176.Square().Square().Square().Square().Square() // a176 = a^(2^138 - 2^50) + a176.Square().Square().Square().Square().Square() // a176 = a^(2^143 - 2^55) + a176.Square().Square().Square().Square().Square() // a176 = a^(2^148 - 2^60) + a176.Square().Square().Square().Square().Square() // a176 = a^(2^153 - 2^65) + a176.Square().Square().Square().Square().Square() // a176 = a^(2^158 - 2^70) + a176.Square().Square().Square().Square().Square() // a176 = a^(2^163 - 2^75) + a176.Square().Square().Square().Square().Square() // a176 = a^(2^168 - 2^80) + a176.Square().Square().Square().Square().Square() // a176 = a^(2^173 - 2^85) + a176.Square().Square().Square() // a176 = a^(2^176 - 2^88) + a176.Mul(&a88) // a176 = a^(2^176 - 1) + a220.SquareVal(&a176).Square().Square().Square().Square() // a220 = a^(2^181 - 2^5) + a220.Square().Square().Square().Square().Square() // a220 = a^(2^186 - 2^10) + a220.Square().Square().Square().Square().Square() // a220 = a^(2^191 - 2^15) + a220.Square().Square().Square().Square().Square() // a220 = a^(2^196 - 2^20) + a220.Square().Square().Square().Square().Square() // a220 = a^(2^201 - 2^25) + a220.Square().Square().Square().Square().Square() // a220 = a^(2^206 - 2^30) + a220.Square().Square().Square().Square().Square() // a220 = a^(2^211 - 2^35) + a220.Square().Square().Square().Square().Square() // a220 = a^(2^216 - 2^40) + a220.Square().Square().Square().Square() // a220 = a^(2^220 - 2^44) + a220.Mul(&a44) // a220 = a^(2^220 - 1) + a223.SquareVal(&a220).Square().Square() // a223 = a^(2^223 - 2^3) + a223.Mul(&a3) // a223 = a^(2^223 - 1) + + f.SquareVal(&a223).Square().Square().Square().Square() // f = a^(2^228 - 2^5) + f.Square().Square().Square().Square().Square() // f = a^(2^233 - 2^10) + f.Square().Square().Square().Square().Square() // f = a^(2^238 - 2^15) + f.Square().Square().Square().Square().Square() // f = a^(2^243 - 2^20) + f.Square().Square().Square() // f = a^(2^246 - 2^23) + f.Mul(&a22) // f = a^(2^246 - 2^22 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^251 - 2^27 - 2^5) + f.Square() // f = a^(2^252 - 2^28 - 2^6) + f.Mul(&a2) // f = a^(2^252 - 2^28 - 2^6 - 2^1 - 1) + f.Square().Square() // f = a^(2^254 - 2^30 - 2^8 - 2^3 - 2^2) + // // = a^(2^254 - 2^30 - 244) + // // = a^((p+1)/4) + + // Ensure the calculated result is actually the square root by squaring it + // and checking against the original value. + var sqr FieldVal + return sqr.SquareVal(f).Normalize().Equals(val.Normalize()) +} + +// Square squares the field value in constant time. The existing field value is +// modified. Note that this function can overflow if multiplying any of the +// individual words exceeds a max uint32. In practice, this means the magnitude +// of the field must be a max of 8 to prevent overflow. +// +// The field value is returned to support chaining. This enables syntax like: +// f.Square().Mul(f2) so that f = f^2 * f2. +// +// Preconditions: +// - The field value MUST have a max magnitude of 8 +// Output Normalized: No +// Output Max Magnitude: 1 +func (f *FieldVal) Square() *FieldVal { + return f.SquareVal(f) +} + +// SquareVal squares the passed value and stores the result in f in constant +// time. Note that this function can overflow if multiplying any of the +// individual words exceeds a max uint32. In practice, this means the magnitude +// of the field being squared must be a max of 8 to prevent overflow. +// +// The field value is returned to support chaining. This enables syntax like: +// f3.SquareVal(f).Mul(f) so that f3 = f^2 * f = f^3. +// +// Preconditions: +// - The input field value MUST have a max magnitude of 8 +// Output Normalized: No +// Output Max Magnitude: 1 +func (f *FieldVal) SquareVal(val *FieldVal) *FieldVal { + // This could be done with a couple of for loops and an array to store + // the intermediate terms, but this unrolled version is significantly + // faster. + + // Terms for 2^(fieldBase*0). + m := uint64(val.n[0]) * uint64(val.n[0]) + t0 := m & fieldBaseMask + + // Terms for 2^(fieldBase*1). + m = (m >> fieldBase) + 2*uint64(val.n[0])*uint64(val.n[1]) + t1 := m & fieldBaseMask + + // Terms for 2^(fieldBase*2). + m = (m >> fieldBase) + + 2*uint64(val.n[0])*uint64(val.n[2]) + + uint64(val.n[1])*uint64(val.n[1]) + t2 := m & fieldBaseMask + + // Terms for 2^(fieldBase*3). + m = (m >> fieldBase) + + 2*uint64(val.n[0])*uint64(val.n[3]) + + 2*uint64(val.n[1])*uint64(val.n[2]) + t3 := m & fieldBaseMask + + // Terms for 2^(fieldBase*4). + m = (m >> fieldBase) + + 2*uint64(val.n[0])*uint64(val.n[4]) + + 2*uint64(val.n[1])*uint64(val.n[3]) + + uint64(val.n[2])*uint64(val.n[2]) + t4 := m & fieldBaseMask + + // Terms for 2^(fieldBase*5). + m = (m >> fieldBase) + + 2*uint64(val.n[0])*uint64(val.n[5]) + + 2*uint64(val.n[1])*uint64(val.n[4]) + + 2*uint64(val.n[2])*uint64(val.n[3]) + t5 := m & fieldBaseMask + + // Terms for 2^(fieldBase*6). + m = (m >> fieldBase) + + 2*uint64(val.n[0])*uint64(val.n[6]) + + 2*uint64(val.n[1])*uint64(val.n[5]) + + 2*uint64(val.n[2])*uint64(val.n[4]) + + uint64(val.n[3])*uint64(val.n[3]) + t6 := m & fieldBaseMask + + // Terms for 2^(fieldBase*7). + m = (m >> fieldBase) + + 2*uint64(val.n[0])*uint64(val.n[7]) + + 2*uint64(val.n[1])*uint64(val.n[6]) + + 2*uint64(val.n[2])*uint64(val.n[5]) + + 2*uint64(val.n[3])*uint64(val.n[4]) + t7 := m & fieldBaseMask + + // Terms for 2^(fieldBase*8). + m = (m >> fieldBase) + + 2*uint64(val.n[0])*uint64(val.n[8]) + + 2*uint64(val.n[1])*uint64(val.n[7]) + + 2*uint64(val.n[2])*uint64(val.n[6]) + + 2*uint64(val.n[3])*uint64(val.n[5]) + + uint64(val.n[4])*uint64(val.n[4]) + t8 := m & fieldBaseMask + + // Terms for 2^(fieldBase*9). + m = (m >> fieldBase) + + 2*uint64(val.n[0])*uint64(val.n[9]) + + 2*uint64(val.n[1])*uint64(val.n[8]) + + 2*uint64(val.n[2])*uint64(val.n[7]) + + 2*uint64(val.n[3])*uint64(val.n[6]) + + 2*uint64(val.n[4])*uint64(val.n[5]) + t9 := m & fieldBaseMask + + // Terms for 2^(fieldBase*10). + m = (m >> fieldBase) + + 2*uint64(val.n[1])*uint64(val.n[9]) + + 2*uint64(val.n[2])*uint64(val.n[8]) + + 2*uint64(val.n[3])*uint64(val.n[7]) + + 2*uint64(val.n[4])*uint64(val.n[6]) + + uint64(val.n[5])*uint64(val.n[5]) + t10 := m & fieldBaseMask + + // Terms for 2^(fieldBase*11). + m = (m >> fieldBase) + + 2*uint64(val.n[2])*uint64(val.n[9]) + + 2*uint64(val.n[3])*uint64(val.n[8]) + + 2*uint64(val.n[4])*uint64(val.n[7]) + + 2*uint64(val.n[5])*uint64(val.n[6]) + t11 := m & fieldBaseMask + + // Terms for 2^(fieldBase*12). + m = (m >> fieldBase) + + 2*uint64(val.n[3])*uint64(val.n[9]) + + 2*uint64(val.n[4])*uint64(val.n[8]) + + 2*uint64(val.n[5])*uint64(val.n[7]) + + uint64(val.n[6])*uint64(val.n[6]) + t12 := m & fieldBaseMask + + // Terms for 2^(fieldBase*13). + m = (m >> fieldBase) + + 2*uint64(val.n[4])*uint64(val.n[9]) + + 2*uint64(val.n[5])*uint64(val.n[8]) + + 2*uint64(val.n[6])*uint64(val.n[7]) + t13 := m & fieldBaseMask + + // Terms for 2^(fieldBase*14). + m = (m >> fieldBase) + + 2*uint64(val.n[5])*uint64(val.n[9]) + + 2*uint64(val.n[6])*uint64(val.n[8]) + + uint64(val.n[7])*uint64(val.n[7]) + t14 := m & fieldBaseMask + + // Terms for 2^(fieldBase*15). + m = (m >> fieldBase) + + 2*uint64(val.n[6])*uint64(val.n[9]) + + 2*uint64(val.n[7])*uint64(val.n[8]) + t15 := m & fieldBaseMask + + // Terms for 2^(fieldBase*16). + m = (m >> fieldBase) + + 2*uint64(val.n[7])*uint64(val.n[9]) + + uint64(val.n[8])*uint64(val.n[8]) + t16 := m & fieldBaseMask + + // Terms for 2^(fieldBase*17). + m = (m >> fieldBase) + 2*uint64(val.n[8])*uint64(val.n[9]) + t17 := m & fieldBaseMask + + // Terms for 2^(fieldBase*18). + m = (m >> fieldBase) + uint64(val.n[9])*uint64(val.n[9]) + t18 := m & fieldBaseMask + + // What's left is for 2^(fieldBase*19). + t19 := m >> fieldBase + + // At this point, all of the terms are grouped into their respective + // base. + // + // Per [HAC] section 14.3.4: Reduction method of moduli of special form, + // when the modulus is of the special form m = b^t - c, highly efficient + // reduction can be achieved per the provided algorithm. + // + // The secp256k1 prime is equivalent to 2^256 - 4294968273, so it fits + // this criteria. + // + // 4294968273 in field representation (base 2^26) is: + // n[0] = 977 + // n[1] = 64 + // That is to say (2^26 * 64) + 977 = 4294968273 + // + // Since each word is in base 26, the upper terms (t10 and up) start + // at 260 bits (versus the final desired range of 256 bits), so the + // field representation of 'c' from above needs to be adjusted for the + // extra 4 bits by multiplying it by 2^4 = 16. 4294968273 * 16 = + // 68719492368. Thus, the adjusted field representation of 'c' is: + // n[0] = 977 * 16 = 15632 + // n[1] = 64 * 16 = 1024 + // That is to say (2^26 * 1024) + 15632 = 68719492368 + // + // To reduce the final term, t19, the entire 'c' value is needed instead + // of only n[0] because there are no more terms left to handle n[1]. + // This means there might be some magnitude left in the upper bits that + // is handled below. + m = t0 + t10*15632 + t0 = m & fieldBaseMask + m = (m >> fieldBase) + t1 + t10*1024 + t11*15632 + t1 = m & fieldBaseMask + m = (m >> fieldBase) + t2 + t11*1024 + t12*15632 + t2 = m & fieldBaseMask + m = (m >> fieldBase) + t3 + t12*1024 + t13*15632 + t3 = m & fieldBaseMask + m = (m >> fieldBase) + t4 + t13*1024 + t14*15632 + t4 = m & fieldBaseMask + m = (m >> fieldBase) + t5 + t14*1024 + t15*15632 + t5 = m & fieldBaseMask + m = (m >> fieldBase) + t6 + t15*1024 + t16*15632 + t6 = m & fieldBaseMask + m = (m >> fieldBase) + t7 + t16*1024 + t17*15632 + t7 = m & fieldBaseMask + m = (m >> fieldBase) + t8 + t17*1024 + t18*15632 + t8 = m & fieldBaseMask + m = (m >> fieldBase) + t9 + t18*1024 + t19*68719492368 + t9 = m & fieldMSBMask + m = m >> fieldMSBBits + + // At this point, if the magnitude is greater than 0, the overall value + // is greater than the max possible 256-bit value. In particular, it is + // "how many times larger" than the max value it is. + // + // The algorithm presented in [HAC] section 14.3.4 repeats until the + // quotient is zero. However, due to the above, we already know at + // least how many times we would need to repeat as it's the value + // currently in m. Thus we can simply multiply the magnitude by the + // field representation of the prime and do a single iteration. Notice + // that nothing will be changed when the magnitude is zero, so we could + // skip this in that case, however always running regardless allows it + // to run in constant time. The final result will be in the range + // 0 <= result <= prime + (2^64 - c), so it is guaranteed to have a + // magnitude of 1, but it is denormalized. + n := t0 + m*977 + f.n[0] = uint32(n & fieldBaseMask) + n = (n >> fieldBase) + t1 + m*64 + f.n[1] = uint32(n & fieldBaseMask) + f.n[2] = uint32((n >> fieldBase) + t2) + f.n[3] = uint32(t3) + f.n[4] = uint32(t4) + f.n[5] = uint32(t5) + f.n[6] = uint32(t6) + f.n[7] = uint32(t7) + f.n[8] = uint32(t8) + f.n[9] = uint32(t9) + + return f +} + +// Inverse finds the modular multiplicative inverse of the field value in +// constant time. The existing field value is modified. +// +// The field value is returned to support chaining. This enables syntax like: +// f.Inverse().Mul(f2) so that f = f^-1 * f2. +// +// Preconditions: +// - The field value MUST have a max magnitude of 8 +// Output Normalized: No +// Output Max Magnitude: 1 +func (f *FieldVal) Inverse() *FieldVal { + // Fermat's little theorem states that for a nonzero number a and prime + // prime p, a^(p-1) = 1 (mod p). Since the multiplicative inverse is + // a*b = 1 (mod p), it follows that b = a*a^(p-2) = a^(p-1) = 1 (mod p). + // Thus, a^(p-2) is the multiplicative inverse. + // + // In order to efficiently compute a^(p-2), p-2 needs to be split into + // a sequence of squares and multiplications that minimizes the number + // of multiplications needed (since they are more costly than + // squarings). Intermediate results are saved and reused as well. + // + // The secp256k1 prime - 2 is 2^256 - 4294968275. + // + // This has a cost of 258 field squarings and 33 field multiplications. + var a2, a3, a4, a10, a11, a21, a42, a45, a63, a1019, a1023 FieldVal + a2.SquareVal(f) + a3.Mul2(&a2, f) + a4.SquareVal(&a2) + a10.SquareVal(&a4).Mul(&a2) + a11.Mul2(&a10, f) + a21.Mul2(&a10, &a11) + a42.SquareVal(&a21) + a45.Mul2(&a42, &a3) + a63.Mul2(&a42, &a21) + a1019.SquareVal(&a63).Square().Square().Square().Mul(&a11) + a1023.Mul2(&a1019, &a4) + f.Set(&a63) // f = a^(2^6 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^11 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^16 - 1024) + f.Mul(&a1023) // f = a^(2^16 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^21 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^26 - 1024) + f.Mul(&a1023) // f = a^(2^26 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^31 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^36 - 1024) + f.Mul(&a1023) // f = a^(2^36 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^41 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^46 - 1024) + f.Mul(&a1023) // f = a^(2^46 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^51 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^56 - 1024) + f.Mul(&a1023) // f = a^(2^56 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^61 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^66 - 1024) + f.Mul(&a1023) // f = a^(2^66 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^71 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^76 - 1024) + f.Mul(&a1023) // f = a^(2^76 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^81 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^86 - 1024) + f.Mul(&a1023) // f = a^(2^86 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^91 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^96 - 1024) + f.Mul(&a1023) // f = a^(2^96 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^101 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^106 - 1024) + f.Mul(&a1023) // f = a^(2^106 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^111 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^116 - 1024) + f.Mul(&a1023) // f = a^(2^116 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^121 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^126 - 1024) + f.Mul(&a1023) // f = a^(2^126 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^131 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^136 - 1024) + f.Mul(&a1023) // f = a^(2^136 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^141 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^146 - 1024) + f.Mul(&a1023) // f = a^(2^146 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^151 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^156 - 1024) + f.Mul(&a1023) // f = a^(2^156 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^161 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^166 - 1024) + f.Mul(&a1023) // f = a^(2^166 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^171 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^176 - 1024) + f.Mul(&a1023) // f = a^(2^176 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^181 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^186 - 1024) + f.Mul(&a1023) // f = a^(2^186 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^191 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^196 - 1024) + f.Mul(&a1023) // f = a^(2^196 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^201 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^206 - 1024) + f.Mul(&a1023) // f = a^(2^206 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^211 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^216 - 1024) + f.Mul(&a1023) // f = a^(2^216 - 1) + f.Square().Square().Square().Square().Square() // f = a^(2^221 - 32) + f.Square().Square().Square().Square().Square() // f = a^(2^226 - 1024) + f.Mul(&a1019) // f = a^(2^226 - 5) + f.Square().Square().Square().Square().Square() // f = a^(2^231 - 160) + f.Square().Square().Square().Square().Square() // f = a^(2^236 - 5120) + f.Mul(&a1023) // f = a^(2^236 - 4097) + f.Square().Square().Square().Square().Square() // f = a^(2^241 - 131104) + f.Square().Square().Square().Square().Square() // f = a^(2^246 - 4195328) + f.Mul(&a1023) // f = a^(2^246 - 4194305) + f.Square().Square().Square().Square().Square() // f = a^(2^251 - 134217760) + f.Square().Square().Square().Square().Square() // f = a^(2^256 - 4294968320) + return f.Mul(&a45) // f = a^(2^256 - 4294968275) = a^(p-2) +} + +// IsGtOrEqPrimeMinusOrder returns whether or not the field value exceeds the +// group order divided by 2 in constant time. +// +// Preconditions: +// - The field value MUST be normalized +func (f *FieldVal) IsGtOrEqPrimeMinusOrder() bool { + // The secp256k1 prime is equivalent to 2^256 - 4294968273 and the group + // order is 2^256 - 432420386565659656852420866394968145599. Thus, + // the prime minus the group order is: + // 432420386565659656852420866390673177326 + // + // In hex that is: + // 0x00000000 00000000 00000000 00000001 45512319 50b75fc4 402da172 2fc9baee + // + // Converting that to field representation (base 2^26) is: + // + // n[0] = 0x03c9baee + // n[1] = 0x03685c8b + // n[2] = 0x01fc4402 + // n[3] = 0x006542dd + // n[4] = 0x01455123 + // + // This can be verified with the following test code: + // pMinusN := new(big.Int).Sub(curveParams.P, curveParams.N) + // var fv FieldVal + // fv.SetByteSlice(pMinusN.Bytes()) + // t.Logf("%x", fv.n) + // + // Outputs: [3c9baee 3685c8b 1fc4402 6542dd 1455123 0 0 0 0 0] + const ( + pMinusNWordZero = 0x03c9baee + pMinusNWordOne = 0x03685c8b + pMinusNWordTwo = 0x01fc4402 + pMinusNWordThree = 0x006542dd + pMinusNWordFour = 0x01455123 + pMinusNWordFive = 0x00000000 + pMinusNWordSix = 0x00000000 + pMinusNWordSeven = 0x00000000 + pMinusNWordEight = 0x00000000 + pMinusNWordNine = 0x00000000 + ) + + // The intuition here is that the value is greater than field prime minus + // the group order if one of the higher individual words is greater than the + // corresponding word and all higher words in the value are equal. + result := constantTimeGreater(f.n[9], pMinusNWordNine) + highWordsEqual := constantTimeEq(f.n[9], pMinusNWordNine) + result |= highWordsEqual & constantTimeGreater(f.n[8], pMinusNWordEight) + highWordsEqual &= constantTimeEq(f.n[8], pMinusNWordEight) + result |= highWordsEqual & constantTimeGreater(f.n[7], pMinusNWordSeven) + highWordsEqual &= constantTimeEq(f.n[7], pMinusNWordSeven) + result |= highWordsEqual & constantTimeGreater(f.n[6], pMinusNWordSix) + highWordsEqual &= constantTimeEq(f.n[6], pMinusNWordSix) + result |= highWordsEqual & constantTimeGreater(f.n[5], pMinusNWordFive) + highWordsEqual &= constantTimeEq(f.n[5], pMinusNWordFive) + result |= highWordsEqual & constantTimeGreater(f.n[4], pMinusNWordFour) + highWordsEqual &= constantTimeEq(f.n[4], pMinusNWordFour) + result |= highWordsEqual & constantTimeGreater(f.n[3], pMinusNWordThree) + highWordsEqual &= constantTimeEq(f.n[3], pMinusNWordThree) + result |= highWordsEqual & constantTimeGreater(f.n[2], pMinusNWordTwo) + highWordsEqual &= constantTimeEq(f.n[2], pMinusNWordTwo) + result |= highWordsEqual & constantTimeGreater(f.n[1], pMinusNWordOne) + highWordsEqual &= constantTimeEq(f.n[1], pMinusNWordOne) + result |= highWordsEqual & constantTimeGreaterOrEq(f.n[0], pMinusNWordZero) + + return result != 0 +} diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/genstatics.go b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/genstatics.go new file mode 100644 index 0000000..fa613de --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/genstatics.go @@ -0,0 +1,196 @@ +// Copyright (c) 2014-2015 The btcsuite developers +// Copyright (c) 2015-2021 The Decred developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +// This file is ignored during the regular build due to the following build tag. +// This build tag is set during go generate. +//go:build gensecp256k1 +// +build gensecp256k1 + +package secp256k1 + +// References: +// [GECC]: Guide to Elliptic Curve Cryptography (Hankerson, Menezes, Vanstone) + +import ( + "encoding/binary" + "math/big" +) + +// compressedBytePoints are dummy points used so the code which generates the +// real values can compile. +var compressedBytePoints = "" + +// SerializedBytePoints returns a serialized byte slice which contains all of +// the possible points per 8-bit window. This is used to when generating +// compressedbytepoints.go. +func SerializedBytePoints() []byte { + // Calculate G^(2^i) for i in 0..255. These are used to avoid recomputing + // them for each digit of the 8-bit windows. + doublingPoints := make([]JacobianPoint, curveParams.BitSize) + var q JacobianPoint + bigAffineToJacobian(curveParams.Gx, curveParams.Gy, &q) + for i := 0; i < curveParams.BitSize; i++ { + // Q = 2*Q. + doublingPoints[i] = q + DoubleNonConst(&q, &q) + } + + // Separate the bits into byte-sized windows. + curveByteSize := curveParams.BitSize / 8 + serialized := make([]byte, curveByteSize*256*2*10*4) + offset := 0 + for byteNum := 0; byteNum < curveByteSize; byteNum++ { + // Grab the 8 bits that make up this byte from doubling points. + startingBit := 8 * (curveByteSize - byteNum - 1) + windowPoints := doublingPoints[startingBit : startingBit+8] + + // Compute all points in this window, convert them to affine, and + // serialize them. + for i := 0; i < 256; i++ { + var point JacobianPoint + for bit := 0; bit < 8; bit++ { + if i>>uint(bit)&1 == 1 { + AddNonConst(&point, &windowPoints[bit], &point) + } + } + point.ToAffine() + + for i := 0; i < len(point.X.n); i++ { + binary.LittleEndian.PutUint32(serialized[offset:], point.X.n[i]) + offset += 4 + } + for i := 0; i < len(point.Y.n); i++ { + binary.LittleEndian.PutUint32(serialized[offset:], point.Y.n[i]) + offset += 4 + } + } + } + + return serialized +} + +// sqrt returns the square root of the provided big integer using Newton's +// method. It's only compiled and used during generation of pre-computed +// values, so speed is not a huge concern. +func sqrt(n *big.Int) *big.Int { + // Initial guess = 2^(log_2(n)/2) + guess := big.NewInt(2) + guess.Exp(guess, big.NewInt(int64(n.BitLen()/2)), nil) + + // Now refine using Newton's method. + big2 := big.NewInt(2) + prevGuess := big.NewInt(0) + for { + prevGuess.Set(guess) + guess.Add(guess, new(big.Int).Div(n, guess)) + guess.Div(guess, big2) + if guess.Cmp(prevGuess) == 0 { + break + } + } + return guess +} + +// EndomorphismVectors runs the first 3 steps of algorithm 3.74 from [GECC] to +// generate the linearly independent vectors needed to generate a balanced +// length-two representation of a multiplier such that k = k1 + k2λ (mod N) and +// returns them. Since the values will always be the same given the fact that N +// and λ are fixed, the final results can be accelerated by storing the +// precomputed values. +func EndomorphismVectors() (a1, b1, a2, b2 *big.Int) { + bigMinus1 := big.NewInt(-1) + + // This section uses an extended Euclidean algorithm to generate a + // sequence of equations: + // s[i] * N + t[i] * λ = r[i] + + nSqrt := sqrt(curveParams.N) + u, v := new(big.Int).Set(curveParams.N), new(big.Int).Set(endomorphismLambda) + x1, y1 := big.NewInt(1), big.NewInt(0) + x2, y2 := big.NewInt(0), big.NewInt(1) + q, r := new(big.Int), new(big.Int) + qu, qx1, qy1 := new(big.Int), new(big.Int), new(big.Int) + s, t := new(big.Int), new(big.Int) + ri, ti := new(big.Int), new(big.Int) + a1, b1, a2, b2 = new(big.Int), new(big.Int), new(big.Int), new(big.Int) + found, oneMore := false, false + for u.Sign() != 0 { + // q = v/u + q.Div(v, u) + + // r = v - q*u + qu.Mul(q, u) + r.Sub(v, qu) + + // s = x2 - q*x1 + qx1.Mul(q, x1) + s.Sub(x2, qx1) + + // t = y2 - q*y1 + qy1.Mul(q, y1) + t.Sub(y2, qy1) + + // v = u, u = r, x2 = x1, x1 = s, y2 = y1, y1 = t + v.Set(u) + u.Set(r) + x2.Set(x1) + x1.Set(s) + y2.Set(y1) + y1.Set(t) + + // As soon as the remainder is less than the sqrt of n, the + // values of a1 and b1 are known. + if !found && r.Cmp(nSqrt) < 0 { + // When this condition executes ri and ti represent the + // r[i] and t[i] values such that i is the greatest + // index for which r >= sqrt(n). Meanwhile, the current + // r and t values are r[i+1] and t[i+1], respectively. + + // a1 = r[i+1], b1 = -t[i+1] + a1.Set(r) + b1.Mul(t, bigMinus1) + found = true + oneMore = true + + // Skip to the next iteration so ri and ti are not + // modified. + continue + + } else if oneMore { + // When this condition executes ri and ti still + // represent the r[i] and t[i] values while the current + // r and t are r[i+2] and t[i+2], respectively. + + // sum1 = r[i]^2 + t[i]^2 + rSquared := new(big.Int).Mul(ri, ri) + tSquared := new(big.Int).Mul(ti, ti) + sum1 := new(big.Int).Add(rSquared, tSquared) + + // sum2 = r[i+2]^2 + t[i+2]^2 + r2Squared := new(big.Int).Mul(r, r) + t2Squared := new(big.Int).Mul(t, t) + sum2 := new(big.Int).Add(r2Squared, t2Squared) + + // if (r[i]^2 + t[i]^2) <= (r[i+2]^2 + t[i+2]^2) + if sum1.Cmp(sum2) <= 0 { + // a2 = r[i], b2 = -t[i] + a2.Set(ri) + b2.Mul(ti, bigMinus1) + } else { + // a2 = r[i+2], b2 = -t[i+2] + a2.Set(r) + b2.Mul(t, bigMinus1) + } + + // All done. + break + } + + ri.Set(r) + ti.Set(t) + } + + return a1, b1, a2, b2 +} diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/loadprecomputed.go b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/loadprecomputed.go new file mode 100644 index 0000000..9f975b5 --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/loadprecomputed.go @@ -0,0 +1,91 @@ +// Copyright 2015 The btcsuite developers +// Copyright (c) 2015-2021 The Decred developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +package secp256k1 + +import ( + "compress/zlib" + "encoding/base64" + "encoding/binary" + "io" + "strings" + "sync" +) + +//go:generate go run -tags gensecp256k1 genprecomps.go + +// bytePointTable describes a table used to house pre-computed values for +// accelerating scalar base multiplication. +type bytePointTable [32][256][2]FieldVal + +// s256BytePoints houses pre-computed values used to accelerate scalar base +// multiplication such that they are only loaded on first use. +var s256BytePoints = func() func() *bytePointTable { + // mustLoadBytePoints decompresses and deserializes the pre-computed byte + // points used to accelerate scalar base multiplication for the secp256k1 + // curve. + // + // This approach is used since it allows the compile to use significantly + // less ram and be performed much faster than it is with hard-coding the + // final in-memory data structure. At the same time, it is quite fast to + // generate the in-memory data structure on first use with this approach + // versus computing the table. + // + // It will panic on any errors because the data is hard coded and thus any + // errors means something is wrong in the source code. + var data *bytePointTable + mustLoadBytePoints := func() { + // There will be no byte points to load when generating them. + bp := compressedBytePoints + if len(bp) == 0 { + return + } + + // Decompress the pre-computed table used to accelerate scalar base + // multiplication. + decoder := base64.NewDecoder(base64.StdEncoding, strings.NewReader(bp)) + r, err := zlib.NewReader(decoder) + if err != nil { + panic(err) + } + serialized, err := io.ReadAll(r) + if err != nil { + panic(err) + } + + // Deserialize the precomputed byte points and set the memory table to + // them. + offset := 0 + var bytePoints bytePointTable + for byteNum := 0; byteNum < len(bytePoints); byteNum++ { + // All points in this window. + for i := 0; i < len(bytePoints[byteNum]); i++ { + px := &bytePoints[byteNum][i][0] + py := &bytePoints[byteNum][i][1] + for i := 0; i < len(px.n); i++ { + px.n[i] = binary.LittleEndian.Uint32(serialized[offset:]) + offset += 4 + } + for i := 0; i < len(py.n); i++ { + py.n[i] = binary.LittleEndian.Uint32(serialized[offset:]) + offset += 4 + } + } + } + data = &bytePoints + } + + // Return a closure that initializes the data on first access. This is done + // because the table takes a non-trivial amount of memory and initializing + // it unconditionally would cause anything that imports the package, either + // directly, or indirectly via transitive deps, to use that memory even if + // the caller never accesses any parts of the package that actually needs + // access to it. + var loadBytePointsOnce sync.Once + return func() *bytePointTable { + loadBytePointsOnce.Do(mustLoadBytePoints) + return data + } +}() diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/modnscalar.go b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/modnscalar.go new file mode 100644 index 0000000..8125d9a --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/modnscalar.go @@ -0,0 +1,1088 @@ +// Copyright (c) 2020 The Decred developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +package secp256k1 + +import ( + "encoding/hex" + "math/big" +) + +// References: +// [SECG]: Recommended Elliptic Curve Domain Parameters +// https://www.secg.org/sec2-v2.pdf +// +// [HAC]: Handbook of Applied Cryptography Menezes, van Oorschot, Vanstone. +// http://cacr.uwaterloo.ca/hac/ + +// Many elliptic curve operations require working with scalars in a finite field +// characterized by the order of the group underlying the secp256k1 curve. +// Given this precision is larger than the biggest available native type, +// obviously some form of bignum math is needed. This code implements +// specialized fixed-precision field arithmetic rather than relying on an +// arbitrary-precision arithmetic package such as math/big for dealing with the +// math modulo the group order since the size is known. As a result, rather +// large performance gains are achieved by taking advantage of many +// optimizations not available to arbitrary-precision arithmetic and generic +// modular arithmetic algorithms. +// +// There are various ways to internally represent each element. For example, +// the most obvious representation would be to use an array of 4 uint64s (64 +// bits * 4 = 256 bits). However, that representation suffers from the fact +// that there is no native Go type large enough to handle the intermediate +// results while adding or multiplying two 64-bit numbers. +// +// Given the above, this implementation represents the field elements as 8 +// uint32s with each word (array entry) treated as base 2^32. This was chosen +// because most systems at the current time are 64-bit (or at least have 64-bit +// registers available for specialized purposes such as MMX) so the intermediate +// results can typically be done using a native register (and using uint64s to +// avoid the need for additional half-word arithmetic) + +const ( + // These fields provide convenient access to each of the words of the + // secp256k1 curve group order N to improve code readability. + // + // The group order of the curve per [SECG] is: + // 0xffffffff ffffffff ffffffff fffffffe baaedce6 af48a03b bfd25e8c d0364141 + orderWordZero uint32 = 0xd0364141 + orderWordOne uint32 = 0xbfd25e8c + orderWordTwo uint32 = 0xaf48a03b + orderWordThree uint32 = 0xbaaedce6 + orderWordFour uint32 = 0xfffffffe + orderWordFive uint32 = 0xffffffff + orderWordSix uint32 = 0xffffffff + orderWordSeven uint32 = 0xffffffff + + // These fields provide convenient access to each of the words of the two's + // complement of the secp256k1 curve group order N to improve code + // readability. + // + // The two's complement of the group order is: + // 0x00000000 00000000 00000000 00000001 45512319 50b75fc4 402da173 2fc9bebf + orderComplementWordZero uint32 = (^orderWordZero) + 1 + orderComplementWordOne uint32 = ^orderWordOne + orderComplementWordTwo uint32 = ^orderWordTwo + orderComplementWordThree uint32 = ^orderWordThree + //orderComplementWordFour uint32 = ^orderWordFour // unused + //orderComplementWordFive uint32 = ^orderWordFive // unused + //orderComplementWordSix uint32 = ^orderWordSix // unused + //orderComplementWordSeven uint32 = ^orderWordSeven // unused + + // These fields provide convenient access to each of the words of the + // secp256k1 curve group order N / 2 to improve code readability and avoid + // the need to recalculate them. + // + // The half order of the secp256k1 curve group is: + // 0x7fffffff ffffffff ffffffff ffffffff 5d576e73 57a4501d dfe92f46 681b20a0 + halfOrderWordZero uint32 = 0x681b20a0 + halfOrderWordOne uint32 = 0xdfe92f46 + halfOrderWordTwo uint32 = 0x57a4501d + halfOrderWordThree uint32 = 0x5d576e73 + halfOrderWordFour uint32 = 0xffffffff + halfOrderWordFive uint32 = 0xffffffff + halfOrderWordSix uint32 = 0xffffffff + halfOrderWordSeven uint32 = 0x7fffffff + + // uint32Mask is simply a mask with all bits set for a uint32 and is used to + // improve the readability of the code. + uint32Mask = 0xffffffff +) + +var ( + // zero32 is an array of 32 bytes used for the purposes of zeroing and is + // defined here to avoid extra allocations. + zero32 = [32]byte{} +) + +// ModNScalar implements optimized 256-bit constant-time fixed-precision +// arithmetic over the secp256k1 group order. This means all arithmetic is +// performed modulo: +// +// 0xfffffffffffffffffffffffffffffffebaaedce6af48a03bbfd25e8cd0364141 +// +// It only implements the arithmetic needed for elliptic curve operations, +// however, the operations that are not implemented can typically be worked +// around if absolutely needed. For example, subtraction can be performed by +// adding the negation. +// +// Should it be absolutely necessary, conversion to the standard library +// math/big.Int can be accomplished by using the Bytes method, slicing the +// resulting fixed-size array, and feeding it to big.Int.SetBytes. However, +// that should typically be avoided when possible as conversion to big.Ints +// requires allocations, is not constant time, and is slower when working modulo +// the group order. +type ModNScalar struct { + // The scalar is represented as 8 32-bit integers in base 2^32. + // + // The following depicts the internal representation: + // --------------------------------------------------------- + // | n[7] | n[6] | ... | n[0] | + // | 32 bits | 32 bits | ... | 32 bits | + // | Mult: 2^(32*7) | Mult: 2^(32*6) | ... | Mult: 2^(32*0) | + // --------------------------------------------------------- + // + // For example, consider the number 2^87 + 2^42 + 1. It would be + // represented as: + // n[0] = 1 + // n[1] = 2^10 + // n[2] = 2^23 + // n[3..7] = 0 + // + // The full 256-bit value is then calculated by looping i from 7..0 and + // doing sum(n[i] * 2^(32i)) like so: + // n[7] * 2^(32*7) = 0 * 2^224 = 0 + // n[6] * 2^(32*6) = 0 * 2^192 = 0 + // ... + // n[2] * 2^(32*2) = 2^23 * 2^64 = 2^87 + // n[1] * 2^(32*1) = 2^10 * 2^32 = 2^42 + // n[0] * 2^(32*0) = 1 * 2^0 = 1 + // Sum: 0 + 0 + ... + 2^87 + 2^42 + 1 = 2^87 + 2^42 + 1 + n [8]uint32 +} + +// String returns the scalar as a human-readable hex string. +// +// This is NOT constant time. +func (s ModNScalar) String() string { + b := s.Bytes() + return hex.EncodeToString(b[:]) +} + +// Set sets the scalar equal to a copy of the passed one in constant time. +// +// The scalar is returned to support chaining. This enables syntax like: +// s := new(ModNScalar).Set(s2).Add(1) so that s = s2 + 1 where s2 is not +// modified. +func (s *ModNScalar) Set(val *ModNScalar) *ModNScalar { + *s = *val + return s +} + +// Zero sets the scalar to zero in constant time. A newly created scalar is +// already set to zero. This function can be useful to clear an existing scalar +// for reuse. +func (s *ModNScalar) Zero() { + s.n[0] = 0 + s.n[1] = 0 + s.n[2] = 0 + s.n[3] = 0 + s.n[4] = 0 + s.n[5] = 0 + s.n[6] = 0 + s.n[7] = 0 +} + +// IsZero returns whether or not the scalar is equal to zero in constant time. +func (s *ModNScalar) IsZero() bool { + // The scalar can only be zero if no bits are set in any of the words. + bits := s.n[0] | s.n[1] | s.n[2] | s.n[3] | s.n[4] | s.n[5] | s.n[6] | s.n[7] + return bits == 0 +} + +// SetInt sets the scalar to the passed integer in constant time. This is a +// convenience function since it is fairly common to perform some arithmetic +// with small native integers. +// +// The scalar is returned to support chaining. This enables syntax like: +// s := new(ModNScalar).SetInt(2).Mul(s2) so that s = 2 * s2. +func (s *ModNScalar) SetInt(ui uint32) *ModNScalar { + s.Zero() + s.n[0] = ui + return s +} + +// constantTimeEq returns 1 if a == b or 0 otherwise in constant time. +func constantTimeEq(a, b uint32) uint32 { + return uint32((uint64(a^b) - 1) >> 63) +} + +// constantTimeNotEq returns 1 if a != b or 0 otherwise in constant time. +func constantTimeNotEq(a, b uint32) uint32 { + return ^uint32((uint64(a^b)-1)>>63) & 1 +} + +// constantTimeLess returns 1 if a < b or 0 otherwise in constant time. +func constantTimeLess(a, b uint32) uint32 { + return uint32((uint64(a) - uint64(b)) >> 63) +} + +// constantTimeLessOrEq returns 1 if a <= b or 0 otherwise in constant time. +func constantTimeLessOrEq(a, b uint32) uint32 { + return uint32((uint64(a) - uint64(b) - 1) >> 63) +} + +// constantTimeGreater returns 1 if a > b or 0 otherwise in constant time. +func constantTimeGreater(a, b uint32) uint32 { + return constantTimeLess(b, a) +} + +// constantTimeGreaterOrEq returns 1 if a >= b or 0 otherwise in constant time. +func constantTimeGreaterOrEq(a, b uint32) uint32 { + return constantTimeLessOrEq(b, a) +} + +// constantTimeMin returns min(a,b) in constant time. +func constantTimeMin(a, b uint32) uint32 { + return b ^ ((a ^ b) & -constantTimeLess(a, b)) +} + +// overflows determines if the current scalar is greater than or equal to the +// group order in constant time and returns 1 if it is or 0 otherwise. +func (s *ModNScalar) overflows() uint32 { + // The intuition here is that the scalar is greater than the group order if + // one of the higher individual words is greater than corresponding word of + // the group order and all higher words in the scalar are equal to their + // corresponding word of the group order. Since this type is modulo the + // group order, being equal is also an overflow back to 0. + // + // Note that the words 5, 6, and 7 are all the max uint32 value, so there is + // no need to test if those individual words of the scalar exceeds them, + // hence, only equality is checked for them. + highWordsEqual := constantTimeEq(s.n[7], orderWordSeven) + highWordsEqual &= constantTimeEq(s.n[6], orderWordSix) + highWordsEqual &= constantTimeEq(s.n[5], orderWordFive) + overflow := highWordsEqual & constantTimeGreater(s.n[4], orderWordFour) + highWordsEqual &= constantTimeEq(s.n[4], orderWordFour) + overflow |= highWordsEqual & constantTimeGreater(s.n[3], orderWordThree) + highWordsEqual &= constantTimeEq(s.n[3], orderWordThree) + overflow |= highWordsEqual & constantTimeGreater(s.n[2], orderWordTwo) + highWordsEqual &= constantTimeEq(s.n[2], orderWordTwo) + overflow |= highWordsEqual & constantTimeGreater(s.n[1], orderWordOne) + highWordsEqual &= constantTimeEq(s.n[1], orderWordOne) + overflow |= highWordsEqual & constantTimeGreaterOrEq(s.n[0], orderWordZero) + + return overflow +} + +// reduce256 reduces the current scalar modulo the group order in accordance +// with the overflows parameter in constant time. The overflows parameter +// specifies whether or not the scalar is known to be greater than the group +// order and MUST either be 1 in the case it is or 0 in the case it is not for a +// correct result. +func (s *ModNScalar) reduce256(overflows uint32) { + // Notice that since s < 2^256 < 2N (where N is the group order), the max + // possible number of reductions required is one. Therefore, in the case a + // reduction is needed, it can be performed with a single subtraction of N. + // Also, recall that subtraction is equivalent to addition by the two's + // complement while ignoring the carry. + // + // When s >= N, the overflows parameter will be 1. Conversely, it will be 0 + // when s < N. Thus multiplying by the overflows parameter will either + // result in 0 or the multiplicand itself. + // + // Combining the above along with the fact that s + 0 = s, the following is + // a constant time implementation that works by either adding 0 or the two's + // complement of N as needed. + // + // The final result will be in the range 0 <= s < N as expected. + overflows64 := uint64(overflows) + c := uint64(s.n[0]) + overflows64*uint64(orderComplementWordZero) + s.n[0] = uint32(c & uint32Mask) + c = (c >> 32) + uint64(s.n[1]) + overflows64*uint64(orderComplementWordOne) + s.n[1] = uint32(c & uint32Mask) + c = (c >> 32) + uint64(s.n[2]) + overflows64*uint64(orderComplementWordTwo) + s.n[2] = uint32(c & uint32Mask) + c = (c >> 32) + uint64(s.n[3]) + overflows64*uint64(orderComplementWordThree) + s.n[3] = uint32(c & uint32Mask) + c = (c >> 32) + uint64(s.n[4]) + overflows64 // * 1 + s.n[4] = uint32(c & uint32Mask) + c = (c >> 32) + uint64(s.n[5]) // + overflows64 * 0 + s.n[5] = uint32(c & uint32Mask) + c = (c >> 32) + uint64(s.n[6]) // + overflows64 * 0 + s.n[6] = uint32(c & uint32Mask) + c = (c >> 32) + uint64(s.n[7]) // + overflows64 * 0 + s.n[7] = uint32(c & uint32Mask) +} + +// SetBytes interprets the provided array as a 256-bit big-endian unsigned +// integer, reduces it modulo the group order, sets the scalar to the result, +// and returns either 1 if it was reduced (aka it overflowed) or 0 otherwise in +// constant time. +// +// Note that a bool is not used here because it is not possible in Go to convert +// from a bool to numeric value in constant time and many constant-time +// operations require a numeric value. +func (s *ModNScalar) SetBytes(b *[32]byte) uint32 { + // Pack the 256 total bits across the 8 uint32 words. This could be done + // with a for loop, but benchmarks show this unrolled version is about 2 + // times faster than the variant that uses a loop. + s.n[0] = uint32(b[31]) | uint32(b[30])<<8 | uint32(b[29])<<16 | uint32(b[28])<<24 + s.n[1] = uint32(b[27]) | uint32(b[26])<<8 | uint32(b[25])<<16 | uint32(b[24])<<24 + s.n[2] = uint32(b[23]) | uint32(b[22])<<8 | uint32(b[21])<<16 | uint32(b[20])<<24 + s.n[3] = uint32(b[19]) | uint32(b[18])<<8 | uint32(b[17])<<16 | uint32(b[16])<<24 + s.n[4] = uint32(b[15]) | uint32(b[14])<<8 | uint32(b[13])<<16 | uint32(b[12])<<24 + s.n[5] = uint32(b[11]) | uint32(b[10])<<8 | uint32(b[9])<<16 | uint32(b[8])<<24 + s.n[6] = uint32(b[7]) | uint32(b[6])<<8 | uint32(b[5])<<16 | uint32(b[4])<<24 + s.n[7] = uint32(b[3]) | uint32(b[2])<<8 | uint32(b[1])<<16 | uint32(b[0])<<24 + + // The value might be >= N, so reduce it as required and return whether or + // not it was reduced. + needsReduce := s.overflows() + s.reduce256(needsReduce) + return needsReduce +} + +// zeroArray32 zeroes the provided 32-byte buffer. +func zeroArray32(b *[32]byte) { + copy(b[:], zero32[:]) +} + +// SetByteSlice interprets the provided slice as a 256-bit big-endian unsigned +// integer (meaning it is truncated to the first 32 bytes), reduces it modulo +// the group order, sets the scalar to the result, and returns whether or not +// the resulting truncated 256-bit integer overflowed in constant time. +// +// Note that since passing a slice with more than 32 bytes is truncated, it is +// possible that the truncated value is less than the order of the curve and +// hence it will not be reported as having overflowed in that case. It is up to +// the caller to decide whether it needs to provide numbers of the appropriate +// size or it is acceptable to use this function with the described truncation +// and overflow behavior. +func (s *ModNScalar) SetByteSlice(b []byte) bool { + var b32 [32]byte + b = b[:constantTimeMin(uint32(len(b)), 32)] + copy(b32[:], b32[:32-len(b)]) + copy(b32[32-len(b):], b) + result := s.SetBytes(&b32) + zeroArray32(&b32) + return result != 0 +} + +// PutBytesUnchecked unpacks the scalar to a 32-byte big-endian value directly +// into the passed byte slice in constant time. The target slice must must have +// at least 32 bytes available or it will panic. +// +// There is a similar function, PutBytes, which unpacks the scalar into a +// 32-byte array directly. This version is provided since it can be useful to +// write directly into part of a larger buffer without needing a separate +// allocation. +// +// Preconditions: +// - The target slice MUST have at least 32 bytes available +func (s *ModNScalar) PutBytesUnchecked(b []byte) { + // Unpack the 256 total bits from the 8 uint32 words. This could be done + // with a for loop, but benchmarks show this unrolled version is about 2 + // times faster than the variant which uses a loop. + b[31] = byte(s.n[0]) + b[30] = byte(s.n[0] >> 8) + b[29] = byte(s.n[0] >> 16) + b[28] = byte(s.n[0] >> 24) + b[27] = byte(s.n[1]) + b[26] = byte(s.n[1] >> 8) + b[25] = byte(s.n[1] >> 16) + b[24] = byte(s.n[1] >> 24) + b[23] = byte(s.n[2]) + b[22] = byte(s.n[2] >> 8) + b[21] = byte(s.n[2] >> 16) + b[20] = byte(s.n[2] >> 24) + b[19] = byte(s.n[3]) + b[18] = byte(s.n[3] >> 8) + b[17] = byte(s.n[3] >> 16) + b[16] = byte(s.n[3] >> 24) + b[15] = byte(s.n[4]) + b[14] = byte(s.n[4] >> 8) + b[13] = byte(s.n[4] >> 16) + b[12] = byte(s.n[4] >> 24) + b[11] = byte(s.n[5]) + b[10] = byte(s.n[5] >> 8) + b[9] = byte(s.n[5] >> 16) + b[8] = byte(s.n[5] >> 24) + b[7] = byte(s.n[6]) + b[6] = byte(s.n[6] >> 8) + b[5] = byte(s.n[6] >> 16) + b[4] = byte(s.n[6] >> 24) + b[3] = byte(s.n[7]) + b[2] = byte(s.n[7] >> 8) + b[1] = byte(s.n[7] >> 16) + b[0] = byte(s.n[7] >> 24) +} + +// PutBytes unpacks the scalar to a 32-byte big-endian value using the passed +// byte array in constant time. +// +// There is a similar function, PutBytesUnchecked, which unpacks the scalar into +// a slice that must have at least 32 bytes available. This version is provided +// since it can be useful to write directly into an array that is type checked. +// +// Alternatively, there is also Bytes, which unpacks the scalar into a new array +// and returns that which can sometimes be more ergonomic in applications that +// aren't concerned about an additional copy. +func (s *ModNScalar) PutBytes(b *[32]byte) { + s.PutBytesUnchecked(b[:]) +} + +// Bytes unpacks the scalar to a 32-byte big-endian value in constant time. +// +// See PutBytes and PutBytesUnchecked for variants that allow an array or slice +// to be passed which can be useful to cut down on the number of allocations +// by allowing the caller to reuse a buffer or write directly into part of a +// larger buffer. +func (s *ModNScalar) Bytes() [32]byte { + var b [32]byte + s.PutBytesUnchecked(b[:]) + return b +} + +// IsOdd returns whether or not the scalar is an odd number in constant time. +func (s *ModNScalar) IsOdd() bool { + // Only odd numbers have the bottom bit set. + return s.n[0]&1 == 1 +} + +// Equals returns whether or not the two scalars are the same in constant time. +func (s *ModNScalar) Equals(val *ModNScalar) bool { + // Xor only sets bits when they are different, so the two scalars can only + // be the same if no bits are set after xoring each word. + bits := (s.n[0] ^ val.n[0]) | (s.n[1] ^ val.n[1]) | (s.n[2] ^ val.n[2]) | + (s.n[3] ^ val.n[3]) | (s.n[4] ^ val.n[4]) | (s.n[5] ^ val.n[5]) | + (s.n[6] ^ val.n[6]) | (s.n[7] ^ val.n[7]) + + return bits == 0 +} + +// Add2 adds the passed two scalars together modulo the group order in constant +// time and stores the result in s. +// +// The scalar is returned to support chaining. This enables syntax like: +// s3.Add2(s, s2).AddInt(1) so that s3 = s + s2 + 1. +func (s *ModNScalar) Add2(val1, val2 *ModNScalar) *ModNScalar { + c := uint64(val1.n[0]) + uint64(val2.n[0]) + s.n[0] = uint32(c & uint32Mask) + c = (c >> 32) + uint64(val1.n[1]) + uint64(val2.n[1]) + s.n[1] = uint32(c & uint32Mask) + c = (c >> 32) + uint64(val1.n[2]) + uint64(val2.n[2]) + s.n[2] = uint32(c & uint32Mask) + c = (c >> 32) + uint64(val1.n[3]) + uint64(val2.n[3]) + s.n[3] = uint32(c & uint32Mask) + c = (c >> 32) + uint64(val1.n[4]) + uint64(val2.n[4]) + s.n[4] = uint32(c & uint32Mask) + c = (c >> 32) + uint64(val1.n[5]) + uint64(val2.n[5]) + s.n[5] = uint32(c & uint32Mask) + c = (c >> 32) + uint64(val1.n[6]) + uint64(val2.n[6]) + s.n[6] = uint32(c & uint32Mask) + c = (c >> 32) + uint64(val1.n[7]) + uint64(val2.n[7]) + s.n[7] = uint32(c & uint32Mask) + + // The result is now 256 bits, but it might still be >= N, so use the + // existing normal reduce method for 256-bit values. + s.reduce256(uint32(c>>32) + s.overflows()) + return s +} + +// Add adds the passed scalar to the existing one modulo the group order in +// constant time and stores the result in s. +// +// The scalar is returned to support chaining. This enables syntax like: +// s.Add(s2).AddInt(1) so that s = s + s2 + 1. +func (s *ModNScalar) Add(val *ModNScalar) *ModNScalar { + return s.Add2(s, val) +} + +// accumulator96 provides a 96-bit accumulator for use in the intermediate +// calculations requiring more than 64-bits. +type accumulator96 struct { + n [3]uint32 +} + +// Add adds the passed unsigned 64-bit value to the accumulator. +func (a *accumulator96) Add(v uint64) { + low := uint32(v & uint32Mask) + hi := uint32(v >> 32) + a.n[0] += low + hi += constantTimeLess(a.n[0], low) // Carry if overflow in n[0]. + a.n[1] += hi + a.n[2] += constantTimeLess(a.n[1], hi) // Carry if overflow in n[1]. +} + +// Rsh32 right shifts the accumulator by 32 bits. +func (a *accumulator96) Rsh32() { + a.n[0] = a.n[1] + a.n[1] = a.n[2] + a.n[2] = 0 +} + +// reduce385 reduces the 385-bit intermediate result in the passed terms modulo +// the group order in constant time and stores the result in s. +func (s *ModNScalar) reduce385(t0, t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11, t12 uint64) { + // At this point, the intermediate result in the passed terms has been + // reduced to fit within 385 bits, so reduce it again using the same method + // described in reduce512. As before, the intermediate result will end up + // being reduced by another 127 bits to 258 bits, thus 9 32-bit terms are + // needed for this iteration. The reduced terms are assigned back to t0 + // through t8. + // + // Note that several of the intermediate calculations require adding 64-bit + // products together which would overflow a uint64, so a 96-bit accumulator + // is used instead until the value is reduced enough to use native uint64s. + + // Terms for 2^(32*0). + var acc accumulator96 + acc.n[0] = uint32(t0) // == acc.Add(t0) because acc is guaranteed to be 0. + acc.Add(t8 * uint64(orderComplementWordZero)) + t0 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*1). + acc.Add(t1) + acc.Add(t8 * uint64(orderComplementWordOne)) + acc.Add(t9 * uint64(orderComplementWordZero)) + t1 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*2). + acc.Add(t2) + acc.Add(t8 * uint64(orderComplementWordTwo)) + acc.Add(t9 * uint64(orderComplementWordOne)) + acc.Add(t10 * uint64(orderComplementWordZero)) + t2 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*3). + acc.Add(t3) + acc.Add(t8 * uint64(orderComplementWordThree)) + acc.Add(t9 * uint64(orderComplementWordTwo)) + acc.Add(t10 * uint64(orderComplementWordOne)) + acc.Add(t11 * uint64(orderComplementWordZero)) + t3 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*4). + acc.Add(t4) + acc.Add(t8) // * uint64(orderComplementWordFour) // * 1 + acc.Add(t9 * uint64(orderComplementWordThree)) + acc.Add(t10 * uint64(orderComplementWordTwo)) + acc.Add(t11 * uint64(orderComplementWordOne)) + acc.Add(t12 * uint64(orderComplementWordZero)) + t4 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*5). + acc.Add(t5) + // acc.Add(t8 * uint64(orderComplementWordFive)) // 0 + acc.Add(t9) // * uint64(orderComplementWordFour) // * 1 + acc.Add(t10 * uint64(orderComplementWordThree)) + acc.Add(t11 * uint64(orderComplementWordTwo)) + acc.Add(t12 * uint64(orderComplementWordOne)) + t5 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*6). + acc.Add(t6) + // acc.Add(t8 * uint64(orderComplementWordSix)) // 0 + // acc.Add(t9 * uint64(orderComplementWordFive)) // 0 + acc.Add(t10) // * uint64(orderComplementWordFour) // * 1 + acc.Add(t11 * uint64(orderComplementWordThree)) + acc.Add(t12 * uint64(orderComplementWordTwo)) + t6 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*7). + acc.Add(t7) + // acc.Add(t8 * uint64(orderComplementWordSeven)) // 0 + // acc.Add(t9 * uint64(orderComplementWordSix)) // 0 + // acc.Add(t10 * uint64(orderComplementWordFive)) // 0 + acc.Add(t11) // * uint64(orderComplementWordFour) // * 1 + acc.Add(t12 * uint64(orderComplementWordThree)) + t7 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*8). + // acc.Add(t9 * uint64(orderComplementWordSeven)) // 0 + // acc.Add(t10 * uint64(orderComplementWordSix)) // 0 + // acc.Add(t11 * uint64(orderComplementWordFive)) // 0 + acc.Add(t12) // * uint64(orderComplementWordFour) // * 1 + t8 = uint64(acc.n[0]) + // acc.Rsh32() // No need since not used after this. Guaranteed to be 0. + + // NOTE: All of the remaining multiplications for this iteration result in 0 + // as they all involve multiplying by combinations of the fifth, sixth, and + // seventh words of the two's complement of N, which are 0, so skip them. + + // At this point, the result is reduced to fit within 258 bits, so reduce it + // again using a slightly modified version of the same method. The maximum + // value in t8 is 2 at this point and therefore multiplying it by each word + // of the two's complement of N and adding it to a 32-bit term will result + // in a maximum requirement of 33 bits, so it is safe to use native uint64s + // here for the intermediate term carry propagation. + // + // Also, since the maximum value in t8 is 2, this ends up reducing by + // another 2 bits to 256 bits. + c := t0 + t8*uint64(orderComplementWordZero) + s.n[0] = uint32(c & uint32Mask) + c = (c >> 32) + t1 + t8*uint64(orderComplementWordOne) + s.n[1] = uint32(c & uint32Mask) + c = (c >> 32) + t2 + t8*uint64(orderComplementWordTwo) + s.n[2] = uint32(c & uint32Mask) + c = (c >> 32) + t3 + t8*uint64(orderComplementWordThree) + s.n[3] = uint32(c & uint32Mask) + c = (c >> 32) + t4 + t8 // * uint64(orderComplementWordFour) == * 1 + s.n[4] = uint32(c & uint32Mask) + c = (c >> 32) + t5 // + t8*uint64(orderComplementWordFive) == 0 + s.n[5] = uint32(c & uint32Mask) + c = (c >> 32) + t6 // + t8*uint64(orderComplementWordSix) == 0 + s.n[6] = uint32(c & uint32Mask) + c = (c >> 32) + t7 // + t8*uint64(orderComplementWordSeven) == 0 + s.n[7] = uint32(c & uint32Mask) + + // The result is now 256 bits, but it might still be >= N, so use the + // existing normal reduce method for 256-bit values. + s.reduce256(uint32(c>>32) + s.overflows()) +} + +// reduce512 reduces the 512-bit intermediate result in the passed terms modulo +// the group order down to 385 bits in constant time and stores the result in s. +func (s *ModNScalar) reduce512(t0, t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11, t12, t13, t14, t15 uint64) { + // At this point, the intermediate result in the passed terms is grouped + // into the respective bases. + // + // Per [HAC] section 14.3.4: Reduction method of moduli of special form, + // when the modulus is of the special form m = b^t - c, where log_2(c) < t, + // highly efficient reduction can be achieved per the provided algorithm. + // + // The secp256k1 group order fits this criteria since it is: + // 2^256 - 432420386565659656852420866394968145599 + // + // Technically the max possible value here is (N-1)^2 since the two scalars + // being multiplied are always mod N. Nevertheless, it is safer to consider + // it to be (2^256-1)^2 = 2^512 - 2^256 + 1 since it is the product of two + // 256-bit values. + // + // The algorithm is to reduce the result modulo the prime by subtracting + // multiples of the group order N. However, in order simplify carry + // propagation, this adds with the two's complement of N to achieve the same + // result. + // + // Since the two's complement of N has 127 leading zero bits, this will end + // up reducing the intermediate result from 512 bits to 385 bits, resulting + // in 13 32-bit terms. The reduced terms are assigned back to t0 through + // t12. + // + // Note that several of the intermediate calculations require adding 64-bit + // products together which would overflow a uint64, so a 96-bit accumulator + // is used instead. + + // Terms for 2^(32*0). + var acc accumulator96 + acc.n[0] = uint32(t0) // == acc.Add(t0) because acc is guaranteed to be 0. + acc.Add(t8 * uint64(orderComplementWordZero)) + t0 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*1). + acc.Add(t1) + acc.Add(t8 * uint64(orderComplementWordOne)) + acc.Add(t9 * uint64(orderComplementWordZero)) + t1 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*2). + acc.Add(t2) + acc.Add(t8 * uint64(orderComplementWordTwo)) + acc.Add(t9 * uint64(orderComplementWordOne)) + acc.Add(t10 * uint64(orderComplementWordZero)) + t2 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*3). + acc.Add(t3) + acc.Add(t8 * uint64(orderComplementWordThree)) + acc.Add(t9 * uint64(orderComplementWordTwo)) + acc.Add(t10 * uint64(orderComplementWordOne)) + acc.Add(t11 * uint64(orderComplementWordZero)) + t3 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*4). + acc.Add(t4) + acc.Add(t8) // * uint64(orderComplementWordFour) // * 1 + acc.Add(t9 * uint64(orderComplementWordThree)) + acc.Add(t10 * uint64(orderComplementWordTwo)) + acc.Add(t11 * uint64(orderComplementWordOne)) + acc.Add(t12 * uint64(orderComplementWordZero)) + t4 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*5). + acc.Add(t5) + // acc.Add(t8 * uint64(orderComplementWordFive)) // 0 + acc.Add(t9) // * uint64(orderComplementWordFour) // * 1 + acc.Add(t10 * uint64(orderComplementWordThree)) + acc.Add(t11 * uint64(orderComplementWordTwo)) + acc.Add(t12 * uint64(orderComplementWordOne)) + acc.Add(t13 * uint64(orderComplementWordZero)) + t5 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*6). + acc.Add(t6) + // acc.Add(t8 * uint64(orderComplementWordSix)) // 0 + // acc.Add(t9 * uint64(orderComplementWordFive)) // 0 + acc.Add(t10) // * uint64(orderComplementWordFour)) // * 1 + acc.Add(t11 * uint64(orderComplementWordThree)) + acc.Add(t12 * uint64(orderComplementWordTwo)) + acc.Add(t13 * uint64(orderComplementWordOne)) + acc.Add(t14 * uint64(orderComplementWordZero)) + t6 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*7). + acc.Add(t7) + // acc.Add(t8 * uint64(orderComplementWordSeven)) // 0 + // acc.Add(t9 * uint64(orderComplementWordSix)) // 0 + // acc.Add(t10 * uint64(orderComplementWordFive)) // 0 + acc.Add(t11) // * uint64(orderComplementWordFour) // * 1 + acc.Add(t12 * uint64(orderComplementWordThree)) + acc.Add(t13 * uint64(orderComplementWordTwo)) + acc.Add(t14 * uint64(orderComplementWordOne)) + acc.Add(t15 * uint64(orderComplementWordZero)) + t7 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*8). + // acc.Add(t9 * uint64(orderComplementWordSeven)) // 0 + // acc.Add(t10 * uint64(orderComplementWordSix)) // 0 + // acc.Add(t11 * uint64(orderComplementWordFive)) // 0 + acc.Add(t12) // * uint64(orderComplementWordFour) // * 1 + acc.Add(t13 * uint64(orderComplementWordThree)) + acc.Add(t14 * uint64(orderComplementWordTwo)) + acc.Add(t15 * uint64(orderComplementWordOne)) + t8 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*9). + // acc.Add(t10 * uint64(orderComplementWordSeven)) // 0 + // acc.Add(t11 * uint64(orderComplementWordSix)) // 0 + // acc.Add(t12 * uint64(orderComplementWordFive)) // 0 + acc.Add(t13) // * uint64(orderComplementWordFour) // * 1 + acc.Add(t14 * uint64(orderComplementWordThree)) + acc.Add(t15 * uint64(orderComplementWordTwo)) + t9 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*10). + // acc.Add(t11 * uint64(orderComplementWordSeven)) // 0 + // acc.Add(t12 * uint64(orderComplementWordSix)) // 0 + // acc.Add(t13 * uint64(orderComplementWordFive)) // 0 + acc.Add(t14) // * uint64(orderComplementWordFour) // * 1 + acc.Add(t15 * uint64(orderComplementWordThree)) + t10 = uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*11). + // acc.Add(t12 * uint64(orderComplementWordSeven)) // 0 + // acc.Add(t13 * uint64(orderComplementWordSix)) // 0 + // acc.Add(t14 * uint64(orderComplementWordFive)) // 0 + acc.Add(t15) // * uint64(orderComplementWordFour) // * 1 + t11 = uint64(acc.n[0]) + acc.Rsh32() + + // NOTE: All of the remaining multiplications for this iteration result in 0 + // as they all involve multiplying by combinations of the fifth, sixth, and + // seventh words of the two's complement of N, which are 0, so skip them. + + // Terms for 2^(32*12). + t12 = uint64(acc.n[0]) + // acc.Rsh32() // No need since not used after this. Guaranteed to be 0. + + // At this point, the result is reduced to fit within 385 bits, so reduce it + // again using the same method accordingly. + s.reduce385(t0, t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11, t12) +} + +// Mul2 multiplies the passed two scalars together modulo the group order in +// constant time and stores the result in s. +// +// The scalar is returned to support chaining. This enables syntax like: +// s3.Mul2(s, s2).AddInt(1) so that s3 = (s * s2) + 1. +func (s *ModNScalar) Mul2(val, val2 *ModNScalar) *ModNScalar { + // This could be done with for loops and an array to store the intermediate + // terms, but this unrolled version is significantly faster. + + // The overall strategy employed here is: + // 1) Calculate the 512-bit product of the two scalars using the standard + // pencil-and-paper method. + // 2) Reduce the result modulo the prime by effectively subtracting + // multiples of the group order N (actually performed by adding multiples + // of the two's complement of N to avoid implementing subtraction). + // 3) Repeat step 2 noting that each iteration reduces the required number + // of bits by 127 because the two's complement of N has 127 leading zero + // bits. + // 4) Once reduced to 256 bits, call the existing reduce method to perform + // a final reduction as needed. + // + // Note that several of the intermediate calculations require adding 64-bit + // products together which would overflow a uint64, so a 96-bit accumulator + // is used instead. + + // Terms for 2^(32*0). + var acc accumulator96 + acc.Add(uint64(val.n[0]) * uint64(val2.n[0])) + t0 := uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*1). + acc.Add(uint64(val.n[0]) * uint64(val2.n[1])) + acc.Add(uint64(val.n[1]) * uint64(val2.n[0])) + t1 := uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*2). + acc.Add(uint64(val.n[0]) * uint64(val2.n[2])) + acc.Add(uint64(val.n[1]) * uint64(val2.n[1])) + acc.Add(uint64(val.n[2]) * uint64(val2.n[0])) + t2 := uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*3). + acc.Add(uint64(val.n[0]) * uint64(val2.n[3])) + acc.Add(uint64(val.n[1]) * uint64(val2.n[2])) + acc.Add(uint64(val.n[2]) * uint64(val2.n[1])) + acc.Add(uint64(val.n[3]) * uint64(val2.n[0])) + t3 := uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*4). + acc.Add(uint64(val.n[0]) * uint64(val2.n[4])) + acc.Add(uint64(val.n[1]) * uint64(val2.n[3])) + acc.Add(uint64(val.n[2]) * uint64(val2.n[2])) + acc.Add(uint64(val.n[3]) * uint64(val2.n[1])) + acc.Add(uint64(val.n[4]) * uint64(val2.n[0])) + t4 := uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*5). + acc.Add(uint64(val.n[0]) * uint64(val2.n[5])) + acc.Add(uint64(val.n[1]) * uint64(val2.n[4])) + acc.Add(uint64(val.n[2]) * uint64(val2.n[3])) + acc.Add(uint64(val.n[3]) * uint64(val2.n[2])) + acc.Add(uint64(val.n[4]) * uint64(val2.n[1])) + acc.Add(uint64(val.n[5]) * uint64(val2.n[0])) + t5 := uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*6). + acc.Add(uint64(val.n[0]) * uint64(val2.n[6])) + acc.Add(uint64(val.n[1]) * uint64(val2.n[5])) + acc.Add(uint64(val.n[2]) * uint64(val2.n[4])) + acc.Add(uint64(val.n[3]) * uint64(val2.n[3])) + acc.Add(uint64(val.n[4]) * uint64(val2.n[2])) + acc.Add(uint64(val.n[5]) * uint64(val2.n[1])) + acc.Add(uint64(val.n[6]) * uint64(val2.n[0])) + t6 := uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*7). + acc.Add(uint64(val.n[0]) * uint64(val2.n[7])) + acc.Add(uint64(val.n[1]) * uint64(val2.n[6])) + acc.Add(uint64(val.n[2]) * uint64(val2.n[5])) + acc.Add(uint64(val.n[3]) * uint64(val2.n[4])) + acc.Add(uint64(val.n[4]) * uint64(val2.n[3])) + acc.Add(uint64(val.n[5]) * uint64(val2.n[2])) + acc.Add(uint64(val.n[6]) * uint64(val2.n[1])) + acc.Add(uint64(val.n[7]) * uint64(val2.n[0])) + t7 := uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*8). + acc.Add(uint64(val.n[1]) * uint64(val2.n[7])) + acc.Add(uint64(val.n[2]) * uint64(val2.n[6])) + acc.Add(uint64(val.n[3]) * uint64(val2.n[5])) + acc.Add(uint64(val.n[4]) * uint64(val2.n[4])) + acc.Add(uint64(val.n[5]) * uint64(val2.n[3])) + acc.Add(uint64(val.n[6]) * uint64(val2.n[2])) + acc.Add(uint64(val.n[7]) * uint64(val2.n[1])) + t8 := uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*9). + acc.Add(uint64(val.n[2]) * uint64(val2.n[7])) + acc.Add(uint64(val.n[3]) * uint64(val2.n[6])) + acc.Add(uint64(val.n[4]) * uint64(val2.n[5])) + acc.Add(uint64(val.n[5]) * uint64(val2.n[4])) + acc.Add(uint64(val.n[6]) * uint64(val2.n[3])) + acc.Add(uint64(val.n[7]) * uint64(val2.n[2])) + t9 := uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*10). + acc.Add(uint64(val.n[3]) * uint64(val2.n[7])) + acc.Add(uint64(val.n[4]) * uint64(val2.n[6])) + acc.Add(uint64(val.n[5]) * uint64(val2.n[5])) + acc.Add(uint64(val.n[6]) * uint64(val2.n[4])) + acc.Add(uint64(val.n[7]) * uint64(val2.n[3])) + t10 := uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*11). + acc.Add(uint64(val.n[4]) * uint64(val2.n[7])) + acc.Add(uint64(val.n[5]) * uint64(val2.n[6])) + acc.Add(uint64(val.n[6]) * uint64(val2.n[5])) + acc.Add(uint64(val.n[7]) * uint64(val2.n[4])) + t11 := uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*12). + acc.Add(uint64(val.n[5]) * uint64(val2.n[7])) + acc.Add(uint64(val.n[6]) * uint64(val2.n[6])) + acc.Add(uint64(val.n[7]) * uint64(val2.n[5])) + t12 := uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*13). + acc.Add(uint64(val.n[6]) * uint64(val2.n[7])) + acc.Add(uint64(val.n[7]) * uint64(val2.n[6])) + t13 := uint64(acc.n[0]) + acc.Rsh32() + + // Terms for 2^(32*14). + acc.Add(uint64(val.n[7]) * uint64(val2.n[7])) + t14 := uint64(acc.n[0]) + acc.Rsh32() + + // What's left is for 2^(32*15). + t15 := uint64(acc.n[0]) + // acc.Rsh32() // No need since not used after this. Guaranteed to be 0. + + // At this point, all of the terms are grouped into their respective base + // and occupy up to 512 bits. Reduce the result accordingly. + s.reduce512(t0, t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11, t12, t13, t14, + t15) + return s +} + +// Mul multiplies the passed scalar with the existing one modulo the group order +// in constant time and stores the result in s. +// +// The scalar is returned to support chaining. This enables syntax like: +// s.Mul(s2).AddInt(1) so that s = (s * s2) + 1. +func (s *ModNScalar) Mul(val *ModNScalar) *ModNScalar { + return s.Mul2(s, val) +} + +// SquareVal squares the passed scalar modulo the group order in constant time +// and stores the result in s. +// +// The scalar is returned to support chaining. This enables syntax like: +// s3.SquareVal(s).Mul(s) so that s3 = s^2 * s = s^3. +func (s *ModNScalar) SquareVal(val *ModNScalar) *ModNScalar { + // This could technically be optimized slightly to take advantage of the + // fact that many of the intermediate calculations in squaring are just + // doubling, however, benchmarking has shown that due to the need to use a + // 96-bit accumulator, any savings are essentially offset by that and + // consequently there is no real difference in performance over just + // multiplying the value by itself to justify the extra code for now. This + // can be revisited in the future if it becomes a bottleneck in practice. + + return s.Mul2(val, val) +} + +// Square squares the scalar modulo the group order in constant time. The +// existing scalar is modified. +// +// The scalar is returned to support chaining. This enables syntax like: +// s.Square().Mul(s2) so that s = s^2 * s2. +func (s *ModNScalar) Square() *ModNScalar { + return s.SquareVal(s) +} + +// NegateVal negates the passed scalar modulo the group order and stores the +// result in s in constant time. +// +// The scalar is returned to support chaining. This enables syntax like: +// s.NegateVal(s2).AddInt(1) so that s = -s2 + 1. +func (s *ModNScalar) NegateVal(val *ModNScalar) *ModNScalar { + // Since the scalar is already in the range 0 <= val < N, where N is the + // group order, negation modulo the group order is just the group order + // minus the value. This implies that the result will always be in the + // desired range with the sole exception of 0 because N - 0 = N itself. + // + // Therefore, in order to avoid the need to reduce the result for every + // other case in order to achieve constant time, this creates a mask that is + // all 0s in the case of the scalar being negated is 0 and all 1s otherwise + // and bitwise ands that mask with each word. + // + // Finally, to simplify the carry propagation, this adds the two's + // complement of the scalar to N in order to achieve the same result. + bits := val.n[0] | val.n[1] | val.n[2] | val.n[3] | val.n[4] | val.n[5] | + val.n[6] | val.n[7] + mask := uint64(uint32Mask * constantTimeNotEq(bits, 0)) + c := uint64(orderWordZero) + (uint64(^val.n[0]) + 1) + s.n[0] = uint32(c & mask) + c = (c >> 32) + uint64(orderWordOne) + uint64(^val.n[1]) + s.n[1] = uint32(c & mask) + c = (c >> 32) + uint64(orderWordTwo) + uint64(^val.n[2]) + s.n[2] = uint32(c & mask) + c = (c >> 32) + uint64(orderWordThree) + uint64(^val.n[3]) + s.n[3] = uint32(c & mask) + c = (c >> 32) + uint64(orderWordFour) + uint64(^val.n[4]) + s.n[4] = uint32(c & mask) + c = (c >> 32) + uint64(orderWordFive) + uint64(^val.n[5]) + s.n[5] = uint32(c & mask) + c = (c >> 32) + uint64(orderWordSix) + uint64(^val.n[6]) + s.n[6] = uint32(c & mask) + c = (c >> 32) + uint64(orderWordSeven) + uint64(^val.n[7]) + s.n[7] = uint32(c & mask) + return s +} + +// Negate negates the scalar modulo the group order in constant time. The +// existing scalar is modified. +// +// The scalar is returned to support chaining. This enables syntax like: +// s.Negate().AddInt(1) so that s = -s + 1. +func (s *ModNScalar) Negate() *ModNScalar { + return s.NegateVal(s) +} + +// InverseValNonConst finds the modular multiplicative inverse of the passed +// scalar and stores result in s in *non-constant* time. +// +// The scalar is returned to support chaining. This enables syntax like: +// s3.InverseVal(s1).Mul(s2) so that s3 = s1^-1 * s2. +func (s *ModNScalar) InverseValNonConst(val *ModNScalar) *ModNScalar { + // This is making use of big integers for now. Ideally it will be replaced + // with an implementation that does not depend on big integers. + valBytes := val.Bytes() + bigVal := new(big.Int).SetBytes(valBytes[:]) + bigVal.ModInverse(bigVal, curveParams.N) + s.SetByteSlice(bigVal.Bytes()) + return s +} + +// InverseNonConst finds the modular multiplicative inverse of the scalar in +// *non-constant* time. The existing scalar is modified. +// +// The scalar is returned to support chaining. This enables syntax like: +// s.Inverse().Mul(s2) so that s = s^-1 * s2. +func (s *ModNScalar) InverseNonConst() *ModNScalar { + return s.InverseValNonConst(s) +} + +// IsOverHalfOrder returns whether or not the scalar exceeds the group order +// divided by 2 in constant time. +func (s *ModNScalar) IsOverHalfOrder() bool { + // The intuition here is that the scalar is greater than half of the group + // order if one of the higher individual words is greater than the + // corresponding word of the half group order and all higher words in the + // scalar are equal to their corresponding word of the half group order. + // + // Note that the words 4, 5, and 6 are all the max uint32 value, so there is + // no need to test if those individual words of the scalar exceeds them, + // hence, only equality is checked for them. + result := constantTimeGreater(s.n[7], halfOrderWordSeven) + highWordsEqual := constantTimeEq(s.n[7], halfOrderWordSeven) + highWordsEqual &= constantTimeEq(s.n[6], halfOrderWordSix) + highWordsEqual &= constantTimeEq(s.n[5], halfOrderWordFive) + highWordsEqual &= constantTimeEq(s.n[4], halfOrderWordFour) + result |= highWordsEqual & constantTimeGreater(s.n[3], halfOrderWordThree) + highWordsEqual &= constantTimeEq(s.n[3], halfOrderWordThree) + result |= highWordsEqual & constantTimeGreater(s.n[2], halfOrderWordTwo) + highWordsEqual &= constantTimeEq(s.n[2], halfOrderWordTwo) + result |= highWordsEqual & constantTimeGreater(s.n[1], halfOrderWordOne) + highWordsEqual &= constantTimeEq(s.n[1], halfOrderWordOne) + result |= highWordsEqual & constantTimeGreater(s.n[0], halfOrderWordZero) + + return result != 0 +} diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/nonce.go b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/nonce.go new file mode 100644 index 0000000..81b205d --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/nonce.go @@ -0,0 +1,263 @@ +// Copyright (c) 2013-2014 The btcsuite developers +// Copyright (c) 2015-2020 The Decred developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +package secp256k1 + +import ( + "bytes" + "crypto/sha256" + "hash" +) + +// References: +// [GECC]: Guide to Elliptic Curve Cryptography (Hankerson, Menezes, Vanstone) +// +// [ISO/IEC 8825-1]: Information technology — ASN.1 encoding rules: +// Specification of Basic Encoding Rules (BER), Canonical Encoding Rules +// (CER) and Distinguished Encoding Rules (DER) +// +// [SEC1]: Elliptic Curve Cryptography (May 31, 2009, Version 2.0) +// https://www.secg.org/sec1-v2.pdf + +var ( + // singleZero is used during RFC6979 nonce generation. It is provided + // here to avoid the need to create it multiple times. + singleZero = []byte{0x00} + + // zeroInitializer is used during RFC6979 nonce generation. It is provided + // here to avoid the need to create it multiple times. + zeroInitializer = bytes.Repeat([]byte{0x00}, sha256.BlockSize) + + // singleOne is used during RFC6979 nonce generation. It is provided + // here to avoid the need to create it multiple times. + singleOne = []byte{0x01} + + // oneInitializer is used during RFC6979 nonce generation. It is provided + // here to avoid the need to create it multiple times. + oneInitializer = bytes.Repeat([]byte{0x01}, sha256.Size) +) + +// hmacsha256 implements a resettable version of HMAC-SHA256. +type hmacsha256 struct { + inner, outer hash.Hash + ipad, opad [sha256.BlockSize]byte +} + +// Write adds data to the running hash. +func (h *hmacsha256) Write(p []byte) { + h.inner.Write(p) +} + +// initKey initializes the HMAC-SHA256 instance to the provided key. +func (h *hmacsha256) initKey(key []byte) { + // Hash the key if it is too large. + if len(key) > sha256.BlockSize { + h.outer.Write(key) + key = h.outer.Sum(nil) + } + copy(h.ipad[:], key) + copy(h.opad[:], key) + for i := range h.ipad { + h.ipad[i] ^= 0x36 + } + for i := range h.opad { + h.opad[i] ^= 0x5c + } + h.inner.Write(h.ipad[:]) +} + +// ResetKey resets the HMAC-SHA256 to its initial state and then initializes it +// with the provided key. It is equivalent to creating a new instance with the +// provided key without allocating more memory. +func (h *hmacsha256) ResetKey(key []byte) { + h.inner.Reset() + h.outer.Reset() + copy(h.ipad[:], zeroInitializer) + copy(h.opad[:], zeroInitializer) + h.initKey(key) +} + +// Resets the HMAC-SHA256 to its initial state using the current key. +func (h *hmacsha256) Reset() { + h.inner.Reset() + h.inner.Write(h.ipad[:]) +} + +// Sum returns the hash of the written data. +func (h *hmacsha256) Sum() []byte { + h.outer.Reset() + h.outer.Write(h.opad[:]) + h.outer.Write(h.inner.Sum(nil)) + return h.outer.Sum(nil) +} + +// newHMACSHA256 returns a new HMAC-SHA256 hasher using the provided key. +func newHMACSHA256(key []byte) *hmacsha256 { + h := new(hmacsha256) + h.inner = sha256.New() + h.outer = sha256.New() + h.initKey(key) + return h +} + +// NonceRFC6979 generates a nonce deterministically according to RFC 6979 using +// HMAC-SHA256 for the hashing function. It takes a 32-byte hash as an input +// and returns a 32-byte nonce to be used for deterministic signing. The extra +// and version arguments are optional, but allow additional data to be added to +// the input of the HMAC. When provided, the extra data must be 32-bytes and +// version must be 16 bytes or they will be ignored. +// +// Finally, the extraIterations parameter provides a method to produce a stream +// of deterministic nonces to ensure the signing code is able to produce a nonce +// that results in a valid signature in the extremely unlikely event the +// original nonce produced results in an invalid signature (e.g. R == 0). +// Signing code should start with 0 and increment it if necessary. +func NonceRFC6979(privKey []byte, hash []byte, extra []byte, version []byte, extraIterations uint32) *ModNScalar { + // Input to HMAC is the 32-byte private key and the 32-byte hash. In + // addition, it may include the optional 32-byte extra data and 16-byte + // version. Create a fixed-size array to avoid extra allocs and slice it + // properly. + const ( + privKeyLen = 32 + hashLen = 32 + extraLen = 32 + versionLen = 16 + ) + var keyBuf [privKeyLen + hashLen + extraLen + versionLen]byte + + // Truncate rightmost bytes of private key and hash if they are too long and + // leave left padding of zeros when they're too short. + if len(privKey) > privKeyLen { + privKey = privKey[:privKeyLen] + } + if len(hash) > hashLen { + hash = hash[:hashLen] + } + offset := privKeyLen - len(privKey) // Zero left padding if needed. + offset += copy(keyBuf[offset:], privKey) + offset += hashLen - len(hash) // Zero left padding if needed. + offset += copy(keyBuf[offset:], hash) + if len(extra) == extraLen { + offset += copy(keyBuf[offset:], extra) + if len(version) == versionLen { + offset += copy(keyBuf[offset:], version) + } + } else if len(version) == versionLen { + // When the version was specified, but not the extra data, leave the + // extra data portion all zero. + offset += privKeyLen + offset += copy(keyBuf[offset:], version) + } + key := keyBuf[:offset] + + // Step B. + // + // V = 0x01 0x01 0x01 ... 0x01 such that the length of V, in bits, is + // equal to 8*ceil(hashLen/8). + // + // Note that since the hash length is a multiple of 8 for the chosen hash + // function in this optimized implementation, the result is just the hash + // length, so avoid the extra calculations. Also, since it isn't modified, + // start with a global value. + v := oneInitializer + + // Step C (Go zeroes all allocated memory). + // + // K = 0x00 0x00 0x00 ... 0x00 such that the length of K, in bits, is + // equal to 8*ceil(hashLen/8). + // + // As above, since the hash length is a multiple of 8 for the chosen hash + // function in this optimized implementation, the result is just the hash + // length, so avoid the extra calculations. + k := zeroInitializer[:hashLen] + + // Step D. + // + // K = HMAC_K(V || 0x00 || int2octets(x) || bits2octets(h1)) + // + // Note that key is the "int2octets(x) || bits2octets(h1)" portion along + // with potential additional data as described by section 3.6 of the RFC. + hasher := newHMACSHA256(k) + hasher.Write(oneInitializer) + hasher.Write(singleZero[:]) + hasher.Write(key) + k = hasher.Sum() + + // Step E. + // + // V = HMAC_K(V) + hasher.ResetKey(k) + hasher.Write(v) + v = hasher.Sum() + + // Step F. + // + // K = HMAC_K(V || 0x01 || int2octets(x) || bits2octets(h1)) + // + // Note that key is the "int2octets(x) || bits2octets(h1)" portion along + // with potential additional data as described by section 3.6 of the RFC. + hasher.Reset() + hasher.Write(v) + hasher.Write(singleOne[:]) + hasher.Write(key[:]) + k = hasher.Sum() + + // Step G. + // + // V = HMAC_K(V) + hasher.ResetKey(k) + hasher.Write(v) + v = hasher.Sum() + + // Step H. + // + // Repeat until the value is nonzero and less than the curve order. + var generated uint32 + for { + // Step H1 and H2. + // + // Set T to the empty sequence. The length of T (in bits) is denoted + // tlen; thus, at that point, tlen = 0. + // + // While tlen < qlen, do the following: + // V = HMAC_K(V) + // T = T || V + // + // Note that because the hash function output is the same length as the + // private key in this optimized implementation, there is no need to + // loop or create an intermediate T. + hasher.Reset() + hasher.Write(v) + v = hasher.Sum() + + // Step H3. + // + // k = bits2int(T) + // If k is within the range [1,q-1], return it. + // + // Otherwise, compute: + // K = HMAC_K(V || 0x00) + // V = HMAC_K(V) + var secret ModNScalar + overflow := secret.SetByteSlice(v) + if !overflow && !secret.IsZero() { + generated++ + if generated > extraIterations { + return &secret + } + } + + // K = HMAC_K(V || 0x00) + hasher.Reset() + hasher.Write(v) + hasher.Write(singleZero[:]) + k = hasher.Sum() + + // V = HMAC_K(V) + hasher.ResetKey(k) + hasher.Write(v) + v = hasher.Sum() + } +} diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/privkey.go b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/privkey.go new file mode 100644 index 0000000..3590346 --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/privkey.go @@ -0,0 +1,77 @@ +// Copyright (c) 2013-2014 The btcsuite developers +// Copyright (c) 2015-2020 The Decred developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +package secp256k1 + +import ( + "crypto/ecdsa" + "crypto/rand" +) + +// PrivateKey provides facilities for working with secp256k1 private keys within +// this package and includes functionality such as serializing and parsing them +// as well as computing their associated public key. +type PrivateKey struct { + Key ModNScalar +} + +// NewPrivateKey instantiates a new private key from a scalar encoded as a +// big integer. +func NewPrivateKey(key *ModNScalar) *PrivateKey { + return &PrivateKey{Key: *key} +} + +// PrivKeyFromBytes returns a private based on the provided byte slice which is +// interpreted as an unsigned 256-bit big-endian integer in the range [0, N-1], +// where N is the order of the curve. +// +// Note that this means passing a slice with more than 32 bytes is truncated and +// that truncated value is reduced modulo N. It is up to the caller to either +// provide a value in the appropriate range or choose to accept the described +// behavior. +// +// Typically callers should simply make use of GeneratePrivateKey when creating +// private keys which properly handles generation of appropriate values. +func PrivKeyFromBytes(privKeyBytes []byte) *PrivateKey { + var privKey PrivateKey + privKey.Key.SetByteSlice(privKeyBytes) + return &privKey +} + +// GeneratePrivateKey returns a private key that is suitable for use with +// secp256k1. +func GeneratePrivateKey() (*PrivateKey, error) { + key, err := ecdsa.GenerateKey(S256(), rand.Reader) + if err != nil { + return nil, err + } + return PrivKeyFromBytes(key.D.Bytes()), nil +} + +// PubKey computes and returns the public key corresponding to this private key. +func (p *PrivateKey) PubKey() *PublicKey { + var result JacobianPoint + ScalarBaseMultNonConst(&p.Key, &result) + result.ToAffine() + return NewPublicKey(&result.X, &result.Y) +} + +// Zero manually clears the memory associated with the private key. This can be +// used to explicitly clear key material from memory for enhanced security +// against memory scraping. +func (p *PrivateKey) Zero() { + p.Key.Zero() +} + +// PrivKeyBytesLen defines the length in bytes of a serialized private key. +const PrivKeyBytesLen = 32 + +// Serialize returns the private key as a 256-bit big-endian binary-encoded +// number, padded to a length of 32 bytes. +func (p PrivateKey) Serialize() []byte { + var privKeyBytes [PrivKeyBytesLen]byte + p.Key.PutBytes(&privKeyBytes) + return privKeyBytes[:] +} diff --git a/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/pubkey.go b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/pubkey.go new file mode 100644 index 0000000..6716583 --- /dev/null +++ b/vendor/github.com/decred/dcrd/dcrec/secp256k1/v4/pubkey.go @@ -0,0 +1,232 @@ +// Copyright (c) 2013-2014 The btcsuite developers +// Copyright (c) 2015-2021 The Decred developers +// Use of this source code is governed by an ISC +// license that can be found in the LICENSE file. + +package secp256k1 + +// References: +// [SEC1] Elliptic Curve Cryptography +// https://www.secg.org/sec1-v2.pdf +// +// [SEC2] Recommended Elliptic Curve Domain Parameters +// https://www.secg.org/sec2-v2.pdf +// +// [ANSI X9.62-1998] Public Key Cryptography For The Financial Services +// Industry: The Elliptic Curve Digital Signature Algorithm (ECDSA) + +import ( + "fmt" +) + +const ( + // PubKeyBytesLenCompressed is the number of bytes of a serialized + // compressed public key. + PubKeyBytesLenCompressed = 33 + + // PubKeyBytesLenUncompressed is the number of bytes of a serialized + // uncompressed public key. + PubKeyBytesLenUncompressed = 65 + + // PubKeyFormatCompressedEven is the identifier prefix byte for a public key + // whose Y coordinate is even when serialized in the compressed format per + // section 2.3.4 of [SEC1](https://secg.org/sec1-v2.pdf#subsubsection.2.3.4). + PubKeyFormatCompressedEven byte = 0x02 + + // PubKeyFormatCompressedOdd is the identifier prefix byte for a public key + // whose Y coordinate is odd when serialized in the compressed format per + // section 2.3.4 of [SEC1](https://secg.org/sec1-v2.pdf#subsubsection.2.3.4). + PubKeyFormatCompressedOdd byte = 0x03 + + // PubKeyFormatUncompressed is the identifier prefix byte for a public key + // when serialized according in the uncompressed format per section 2.3.3 of + // [SEC1](https://secg.org/sec1-v2.pdf#subsubsection.2.3.3). + PubKeyFormatUncompressed byte = 0x04 + + // PubKeyFormatHybridEven is the identifier prefix byte for a public key + // whose Y coordinate is even when serialized according to the hybrid format + // per section 4.3.6 of [ANSI X9.62-1998]. + // + // NOTE: This format makes little sense in practice an therefore this + // package will not produce public keys serialized in this format. However, + // it will parse them since they exist in the wild. + PubKeyFormatHybridEven byte = 0x06 + + // PubKeyFormatHybridOdd is the identifier prefix byte for a public key + // whose Y coordingate is odd when serialized according to the hybrid format + // per section 4.3.6 of [ANSI X9.62-1998]. + // + // NOTE: This format makes little sense in practice an therefore this + // package will not produce public keys serialized in this format. However, + // it will parse them since they exist in the wild. + PubKeyFormatHybridOdd byte = 0x07 +) + +// PublicKey provides facilities for efficiently working with secp256k1 public +// keys within this package and includes functions to serialize in both +// uncompressed and compressed SEC (Standards for Efficient Cryptography) +// formats. +type PublicKey struct { + x FieldVal + y FieldVal +} + +// NewPublicKey instantiates a new public key with the given x and y +// coordinates. +// +// It should be noted that, unlike ParsePubKey, since this accepts arbitrary x +// and y coordinates, it allows creation of public keys that are not valid +// points on the secp256k1 curve. The IsOnCurve method of the returned instance +// can be used to determine validity. +func NewPublicKey(x, y *FieldVal) *PublicKey { + var pubKey PublicKey + pubKey.x.Set(x) + pubKey.y.Set(y) + return &pubKey +} + +// ParsePubKey parses a secp256k1 public key encoded according to the format +// specified by ANSI X9.62-1998, which means it is also compatible with the +// SEC (Standards for Efficient Cryptography) specification which is a subset of +// the former. In other words, it supports the uncompressed, compressed, and +// hybrid formats as follows: +// +// Compressed: +// <32-byte X coordinate> +// Uncompressed: +// <32-byte X coordinate><32-byte Y coordinate> +// Hybrid: +// <32-byte X coordinate><32-byte Y coordinate> +// +// NOTE: The hybrid format makes little sense in practice an therefore this +// package will not produce public keys serialized in this format. However, +// this function will properly parse them since they exist in the wild. +func ParsePubKey(serialized []byte) (key *PublicKey, err error) { + var x, y FieldVal + switch len(serialized) { + case PubKeyBytesLenUncompressed: + // Reject unsupported public key formats for the given length. + format := serialized[0] + switch format { + case PubKeyFormatUncompressed: + case PubKeyFormatHybridEven, PubKeyFormatHybridOdd: + default: + str := fmt.Sprintf("invalid public key: unsupported format: %x", + format) + return nil, makeError(ErrPubKeyInvalidFormat, str) + } + + // Parse the x and y coordinates while ensuring that they are in the + // allowed range. + if overflow := x.SetByteSlice(serialized[1:33]); overflow { + str := "invalid public key: x >= field prime" + return nil, makeError(ErrPubKeyXTooBig, str) + } + if overflow := y.SetByteSlice(serialized[33:]); overflow { + str := "invalid public key: y >= field prime" + return nil, makeError(ErrPubKeyYTooBig, str) + } + + // Ensure the oddness of the y coordinate matches the specified format + // for hybrid public keys. + if format == PubKeyFormatHybridEven || format == PubKeyFormatHybridOdd { + wantOddY := format == PubKeyFormatHybridOdd + if y.IsOdd() != wantOddY { + str := fmt.Sprintf("invalid public key: y oddness does not "+ + "match specified value of %v", wantOddY) + return nil, makeError(ErrPubKeyMismatchedOddness, str) + } + } + + // Reject public keys that are not on the secp256k1 curve. + if !isOnCurve(&x, &y) { + str := fmt.Sprintf("invalid public key: [%v,%v] not on secp256k1 "+ + "curve", x, y) + return nil, makeError(ErrPubKeyNotOnCurve, str) + } + + case PubKeyBytesLenCompressed: + // Reject unsupported public key formats for the given length. + format := serialized[0] + switch format { + case PubKeyFormatCompressedEven, PubKeyFormatCompressedOdd: + default: + str := fmt.Sprintf("invalid public key: unsupported format: %x", + format) + return nil, makeError(ErrPubKeyInvalidFormat, str) + } + + // Parse the x coordinate while ensuring that it is in the allowed + // range. + if overflow := x.SetByteSlice(serialized[1:33]); overflow { + str := "invalid public key: x >= field prime" + return nil, makeError(ErrPubKeyXTooBig, str) + } + + // Attempt to calculate the y coordinate for the given x coordinate such + // that the result pair is a point on the secp256k1 curve and the + // solution with desired oddness is chosen. + wantOddY := format == PubKeyFormatCompressedOdd + if !DecompressY(&x, wantOddY, &y) { + str := fmt.Sprintf("invalid public key: x coordinate %v is not on "+ + "the secp256k1 curve", x) + return nil, makeError(ErrPubKeyNotOnCurve, str) + } + y.Normalize() + + default: + str := fmt.Sprintf("malformed public key: invalid length: %d", + len(serialized)) + return nil, makeError(ErrPubKeyInvalidLen, str) + } + + return NewPublicKey(&x, &y), nil +} + +// SerializeUncompressed serializes a public key in the 65-byte uncompressed +// format. +func (p PublicKey) SerializeUncompressed() []byte { + // 0x04 || 32-byte x coordinate || 32-byte y coordinate + var b [PubKeyBytesLenUncompressed]byte + b[0] = PubKeyFormatUncompressed + p.x.PutBytesUnchecked(b[1:33]) + p.y.PutBytesUnchecked(b[33:65]) + return b[:] +} + +// SerializeCompressed serializes a public key in the 33-byte compressed format. +func (p PublicKey) SerializeCompressed() []byte { + // Choose the format byte depending on the oddness of the Y coordinate. + format := PubKeyFormatCompressedEven + if p.y.IsOdd() { + format = PubKeyFormatCompressedOdd + } + + // 0x02 or 0x03 || 32-byte x coordinate + var b [PubKeyBytesLenCompressed]byte + b[0] = format + p.x.PutBytesUnchecked(b[1:33]) + return b[:] +} + +// IsEqual compares this PublicKey instance to the one passed, returning true if +// both PublicKeys are equivalent. A PublicKey is equivalent to another, if they +// both have the same X and Y coordinate. +func (p *PublicKey) IsEqual(otherPubKey *PublicKey) bool { + return p.x.Equals(&otherPubKey.x) && p.y.Equals(&otherPubKey.y) +} + +// AsJacobian converts the public key into a Jacobian point with Z=1 and stores +// the result in the provided result param. This allows the public key to be +// treated a Jacobian point in the secp256k1 group in calculations. +func (p *PublicKey) AsJacobian(result *JacobianPoint) { + result.X.Set(&p.x) + result.Y.Set(&p.y) + result.Z.SetInt(1) +} + +// IsOnCurve returns whether or not the public key represents a point on the +// secp256k1 curve. +func (p *PublicKey) IsOnCurve() bool { + return isOnCurve(&p.x, &p.y) +} diff --git a/vendor/github.com/tyler-smith/go-bip39/.travis.yml b/vendor/github.com/tyler-smith/go-bip39/.travis.yml index 2b48ff1..65041c9 100644 --- a/vendor/github.com/tyler-smith/go-bip39/.travis.yml +++ b/vendor/github.com/tyler-smith/go-bip39/.travis.yml @@ -1,12 +1,13 @@ language: go go: - - "1.6.x" - - "1.7.x" - - "1.8.x" - - "1.9.x" - - "1.10.x" - - "release" + - "1.11.x" + - "1.12.x" + - "1.13.x" + - "1.14.x" - "tip" +env: + - GO111MODULE=on + script: - make profile_tests diff --git a/vendor/github.com/tyler-smith/go-bip39/Gopkg.lock b/vendor/github.com/tyler-smith/go-bip39/Gopkg.lock deleted file mode 100644 index e15d33f..0000000 --- a/vendor/github.com/tyler-smith/go-bip39/Gopkg.lock +++ /dev/null @@ -1,15 +0,0 @@ -# This file is autogenerated, do not edit; changes may be undone by the next 'dep ensure'. - - -[[projects]] - branch = "master" - name = "golang.org/x/crypto" - packages = ["pbkdf2"] - revision = "a49355c7e3f8fe157a85be2f77e6e269a0f89602" - -[solve-meta] - analyzer-name = "dep" - analyzer-version = 1 - inputs-digest = "d7f1a7207c39125afcb9ca2365832cb83458edfc17f2f7e8d28fd56f19436856" - solver-name = "gps-cdcl" - solver-version = 1 diff --git a/vendor/github.com/tyler-smith/go-bip39/Gopkg.toml b/vendor/github.com/tyler-smith/go-bip39/Gopkg.toml deleted file mode 100644 index 2f655b2..0000000 --- a/vendor/github.com/tyler-smith/go-bip39/Gopkg.toml +++ /dev/null @@ -1,26 +0,0 @@ - -# Gopkg.toml example -# -# Refer to https://github.com/golang/dep/blob/master/docs/Gopkg.toml.md -# for detailed Gopkg.toml documentation. -# -# required = ["github.com/user/thing/cmd/thing"] -# ignored = ["github.com/user/project/pkgX", "bitbucket.org/user/project/pkgA/pkgY"] -# -# [[constraint]] -# name = "github.com/user/project" -# version = "1.0.0" -# -# [[constraint]] -# name = "github.com/user/project2" -# branch = "dev" -# source = "github.com/myfork/project2" -# -# [[override]] -# name = "github.com/x/y" -# version = "2.4.0" - - -[[constraint]] - branch = "master" - name = "golang.org/x/crypto" diff --git a/vendor/github.com/tyler-smith/go-bip39/bip39.go b/vendor/github.com/tyler-smith/go-bip39/bip39.go index 62503b0..180adda 100644 --- a/vendor/github.com/tyler-smith/go-bip39/bip39.go +++ b/vendor/github.com/tyler-smith/go-bip39/bip39.go @@ -273,25 +273,8 @@ func NewSeed(mnemonic string, password string) []byte { // Validity is determined by both the number of words being appropriate, // and that all the words in the mnemonic are present in the word list. func IsMnemonicValid(mnemonic string) bool { - // Create a list of all the words in the mnemonic sentence - words := strings.Fields(mnemonic) - - // Get word count - wordCount := len(words) - - // The number of words should be 12, 15, 18, 21 or 24 - if wordCount%3 != 0 || wordCount < 12 || wordCount > 24 { - return false - } - - // Check if all words belong in the wordlist - for _, word := range words { - if _, ok := wordMap[word]; !ok { - return false - } - } - - return true + _, err := EntropyFromMnemonic(mnemonic) + return err == nil } // Appends to data the first (len(data) / 32)bits of the result of sha256(data) diff --git a/vendor/github.com/tyler-smith/go-bip39/wordlists/czech.go b/vendor/github.com/tyler-smith/go-bip39/wordlists/czech.go new file mode 100644 index 0000000..284dcf1 --- /dev/null +++ b/vendor/github.com/tyler-smith/go-bip39/wordlists/czech.go @@ -0,0 +1,2071 @@ +package wordlists + +import ( + "fmt" + "hash/crc32" + "strings" +) + +func init() { + // Ensure word list is correct + // $ wget https://raw.githubusercontent.com/bitcoin/bips/master/bip-0039/czech.txt + // $ crc32 czech.txt + // d1b5fda0 + checksum := crc32.ChecksumIEEE([]byte(czech)) + if fmt.Sprintf("%x", checksum) != "d1b5fda0" { + panic("czech checksum invalid") + } +} + +// Czech is a slice of mnemonic words taken from the bip39 specification +// https://raw.githubusercontent.com/bitcoin/bips/master/bip-0039/czech.txt +var Czech = strings.Split(strings.TrimSpace(czech), "\n") +var czech = `abdikace +abeceda +adresa +agrese +akce +aktovka +alej +alkohol +amputace +ananas +andulka +anekdota +anketa +antika +anulovat +archa +arogance +asfalt +asistent +aspirace +astma +astronom +atlas +atletika +atol +autobus +azyl +babka +bachor +bacil +baculka +badatel +bageta +bagr +bahno +bakterie +balada +baletka +balkon +balonek +balvan +balza +bambus +bankomat +barbar +baret +barman +baroko +barva +baterka +batoh +bavlna +bazalka +bazilika +bazuka +bedna +beran +beseda +bestie +beton +bezinka +bezmoc +beztak +bicykl 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+svatba +svazek +svisle +svitek +svoboda +svodidlo +svorka +svrab +sykavka +sykot +synek +synovec +sypat +sypkost +syrovost +sysel +sytost +tabletka +tabule +tahoun +tajemno +tajfun +tajga +tajit +tajnost +taktika +tamhle +tampon +tancovat +tanec +tanker +tapeta +tavenina +tazatel +technika +tehdy +tekutina +telefon +temnota +tendence +tenista +tenor +teplota +tepna +teprve +terapie +termoska +textil +ticho +tiskopis +titulek +tkadlec +tkanina +tlapka +tleskat +tlukot +tlupa +tmel +toaleta +topinka +topol +torzo +touha +toulec +tradice +traktor +tramp +trasa +traverza +trefit +trest +trezor +trhavina +trhlina +trochu +trojice +troska +trouba +trpce +trpitel +trpkost +trubec +truchlit +truhlice +trus +trvat +tudy +tuhnout +tuhost +tundra +turista +turnaj +tuzemsko +tvaroh +tvorba +tvrdost +tvrz +tygr +tykev +ubohost +uboze +ubrat +ubrousek +ubrus +ubytovna +ucho +uctivost +udivit +uhradit +ujednat +ujistit +ujmout +ukazatel +uklidnit +uklonit +ukotvit +ukrojit +ulice +ulita +ulovit +umyvadlo +unavit +uniforma +uniknout +upadnout +uplatnit +uplynout +upoutat +upravit +uran +urazit +usednout +usilovat +usmrtit +usnadnit +usnout +usoudit +ustlat +ustrnout +utahovat +utkat +utlumit +utonout +utopenec +utrousit +uvalit +uvolnit +uvozovka +uzdravit +uzel +uzenina +uzlina +uznat +vagon +valcha +valoun +vana +vandal +vanilka +varan +varhany +varovat +vcelku +vchod +vdova +vedro +vegetace +vejce +velbloud +veletrh +velitel +velmoc +velryba +venkov +veranda +verze +veselka +veskrze +vesnice +vespodu +vesta +veterina +veverka +vibrace +vichr +videohra +vidina +vidle +vila +vinice +viset +vitalita +vize +vizitka +vjezd +vklad +vkus +vlajka +vlak +vlasec +vlevo +vlhkost +vliv +vlnovka +vloupat +vnucovat +vnuk +voda +vodivost +vodoznak +vodstvo +vojensky +vojna +vojsko +volant +volba +volit +volno +voskovka +vozidlo +vozovna +vpravo +vrabec +vracet +vrah +vrata +vrba +vrcholek +vrhat +vrstva +vrtule +vsadit +vstoupit +vstup +vtip +vybavit +vybrat +vychovat +vydat +vydra +vyfotit +vyhledat +vyhnout +vyhodit +vyhradit +vyhubit +vyjasnit +vyjet +vyjmout +vyklopit +vykonat +vylekat +vymazat +vymezit +vymizet +vymyslet +vynechat +vynikat +vynutit +vypadat +vyplatit +vypravit +vypustit +vyrazit +vyrovnat +vyrvat +vyslovit +vysoko +vystavit +vysunout +vysypat +vytasit +vytesat +vytratit +vyvinout +vyvolat +vyvrhel +vyzdobit +vyznat +vzadu +vzbudit +vzchopit +vzdor +vzduch +vzdychat +vzestup +vzhledem +vzkaz +vzlykat +vznik +vzorek +vzpoura +vztah +vztek +xylofon +zabrat +zabydlet +zachovat +zadarmo +zadusit +zafoukat +zahltit +zahodit +zahrada +zahynout +zajatec +zajet +zajistit +zaklepat +zakoupit +zalepit +zamezit +zamotat +zamyslet +zanechat +zanikat +zaplatit +zapojit +zapsat +zarazit +zastavit +zasunout +zatajit +zatemnit +zatknout +zaujmout +zavalit +zavelet +zavinit +zavolat +zavrtat +zazvonit +zbavit +zbrusu +zbudovat +zbytek +zdaleka +zdarma +zdatnost +zdivo +zdobit +zdroj +zdvih +zdymadlo +zelenina +zeman +zemina +zeptat +zezadu +zezdola +zhatit +zhltnout +zhluboka +zhotovit +zhruba +zima +zimnice +zjemnit +zklamat +zkoumat +zkratka +zkumavka +zlato +zlehka +zloba +zlom +zlost +zlozvyk +zmapovat +zmar +zmatek +zmije +zmizet +zmocnit +zmodrat +zmrzlina +zmutovat +znak +znalost +znamenat +znovu +zobrazit +zotavit +zoubek +zoufale +zplodit +zpomalit +zprava +zprostit +zprudka +zprvu +zrada +zranit +zrcadlo +zrnitost +zrno +zrovna +zrychlit +zrzavost +zticha +ztratit +zubovina +zubr +zvednout +zvenku +zvesela +zvon +zvrat +zvukovod +zvyk +` diff --git a/vendor/golang.org/x/crypto/pbkdf2/pbkdf2.go b/vendor/golang.org/x/crypto/pbkdf2/pbkdf2.go index 593f653..904b57e 100644 --- a/vendor/golang.org/x/crypto/pbkdf2/pbkdf2.go +++ b/vendor/golang.org/x/crypto/pbkdf2/pbkdf2.go @@ -32,7 +32,7 @@ import ( // can get a derived key for e.g. AES-256 (which needs a 32-byte key) by // doing: // -// dk := pbkdf2.Key([]byte("some password"), salt, 4096, 32, sha1.New) +// dk := pbkdf2.Key([]byte("some password"), salt, 4096, 32, sha1.New) // // Remember to get a good random salt. At least 8 bytes is recommended by the // RFC. diff --git a/vendor/golang.org/x/crypto/sha3/doc.go b/vendor/golang.org/x/crypto/sha3/doc.go index c2fef30..decd8cf 100644 --- a/vendor/golang.org/x/crypto/sha3/doc.go +++ b/vendor/golang.org/x/crypto/sha3/doc.go @@ -8,8 +8,7 @@ // Both types of hash function use the "sponge" construction and the Keccak // permutation. For a detailed specification see http://keccak.noekeon.org/ // -// -// Guidance +// # Guidance // // If you aren't sure what function you need, use SHAKE256 with at least 64 // bytes of output. The SHAKE instances are faster than the SHA3 instances; @@ -19,8 +18,7 @@ // secret key to the input, hash with SHAKE256 and read at least 32 bytes of // output. // -// -// Security strengths +// # Security strengths // // The SHA3-x (x equals 224, 256, 384, or 512) functions have a security // strength against preimage attacks of x bits. Since they only produce "x" @@ -31,8 +29,7 @@ // is used. Requesting more than 64 or 32 bytes of output, respectively, does // not increase the collision-resistance of the SHAKE functions. // -// -// The sponge construction +// # The sponge construction // // A sponge builds a pseudo-random function from a public pseudo-random // permutation, by applying the permutation to a state of "rate + capacity" @@ -50,8 +47,7 @@ // Since the KeccakF-1600 permutation is 1600 bits (200 bytes) wide, this means // that the security strength of a sponge instance is equal to (1600 - bitrate) / 2. // -// -// Recommendations +// # Recommendations // // The SHAKE functions are recommended for most new uses. They can produce // output of arbitrary length. SHAKE256, with an output length of at least diff --git a/vendor/golang.org/x/crypto/sha3/keccakf_amd64.s b/vendor/golang.org/x/crypto/sha3/keccakf_amd64.s index 8f4d187..4cfa543 100644 --- a/vendor/golang.org/x/crypto/sha3/keccakf_amd64.s +++ b/vendor/golang.org/x/crypto/sha3/keccakf_amd64.s @@ -2,6 +2,7 @@ // Use of this source code is governed by a BSD-style // license that can be found in the LICENSE file. +//go:build amd64 && !purego && gc // +build amd64,!purego,gc // This code was translated into a form compatible with 6a from the public diff --git a/vendor/golang.org/x/crypto/sha3/sha3.go b/vendor/golang.org/x/crypto/sha3/sha3.go index ba269a0..fa182be 100644 --- a/vendor/golang.org/x/crypto/sha3/sha3.go +++ b/vendor/golang.org/x/crypto/sha3/sha3.go @@ -86,7 +86,7 @@ func (d *state) permute() { d.buf = d.storage.asBytes()[:0] keccakF1600(&d.a) case spongeSqueezing: - // If we're squeezing, we need to apply the permutatin before + // If we're squeezing, we need to apply the permutation before // copying more output. keccakF1600(&d.a) d.buf = d.storage.asBytes()[:d.rate] diff --git a/vendor/golang.org/x/crypto/sha3/sha3_s390x.go b/vendor/golang.org/x/crypto/sha3/sha3_s390x.go index 4fcfc92..63a3edb 100644 --- a/vendor/golang.org/x/crypto/sha3/sha3_s390x.go +++ b/vendor/golang.org/x/crypto/sha3/sha3_s390x.go @@ -34,11 +34,13 @@ const ( // kimd is a wrapper for the 'compute intermediate message digest' instruction. // src must be a multiple of the rate for the given function code. +// //go:noescape func kimd(function code, chain *[200]byte, src []byte) // klmd is a wrapper for the 'compute last message digest' instruction. // src padding is handled by the instruction. +// //go:noescape func klmd(function code, chain *[200]byte, dst, src []byte) diff --git a/vendor/golang.org/x/crypto/sha3/sha3_s390x.s b/vendor/golang.org/x/crypto/sha3/sha3_s390x.s index e2df641..a0e051b 100644 --- a/vendor/golang.org/x/crypto/sha3/sha3_s390x.s +++ b/vendor/golang.org/x/crypto/sha3/sha3_s390x.s @@ -2,6 +2,7 @@ // Use of this source code is governed by a BSD-style // license that can be found in the LICENSE file. +//go:build gc && !purego // +build gc,!purego #include "textflag.h" diff --git a/vendor/golang.org/x/crypto/sha3/xor_generic.go b/vendor/golang.org/x/crypto/sha3/xor_generic.go index fd35f02..8d94771 100644 --- a/vendor/golang.org/x/crypto/sha3/xor_generic.go +++ b/vendor/golang.org/x/crypto/sha3/xor_generic.go @@ -19,7 +19,7 @@ func xorInGeneric(d *state, buf []byte) { } } -// copyOutGeneric copies ulint64s to a byte buffer. +// copyOutGeneric copies uint64s to a byte buffer. func copyOutGeneric(d *state, b []byte) { for i := 0; len(b) >= 8; i++ { binary.LittleEndian.PutUint64(b, d.a[i]) diff --git a/vendor/golang.org/x/sys/cpu/asm_aix_ppc64.s b/vendor/golang.org/x/sys/cpu/asm_aix_ppc64.s index 6b4027b..db9171c 100644 --- a/vendor/golang.org/x/sys/cpu/asm_aix_ppc64.s +++ b/vendor/golang.org/x/sys/cpu/asm_aix_ppc64.s @@ -2,6 +2,7 @@ // Use of this source code is governed by a BSD-style // license that can be found in the LICENSE file. +//go:build gc // +build gc #include "textflag.h" diff --git a/vendor/golang.org/x/sys/cpu/byteorder.go b/vendor/golang.org/x/sys/cpu/byteorder.go index dcbb14e..271055b 100644 --- a/vendor/golang.org/x/sys/cpu/byteorder.go +++ b/vendor/golang.org/x/sys/cpu/byteorder.go @@ -46,6 +46,7 @@ func hostByteOrder() byteOrder { case "386", "amd64", "amd64p32", "alpha", "arm", "arm64", + "loong64", "mipsle", "mips64le", "mips64p32le", "nios2", "ppc64le", diff --git a/vendor/golang.org/x/sys/cpu/cpu.go b/vendor/golang.org/x/sys/cpu/cpu.go index f77701f..83f112c 100644 --- a/vendor/golang.org/x/sys/cpu/cpu.go +++ b/vendor/golang.org/x/sys/cpu/cpu.go @@ -56,6 +56,7 @@ var X86 struct { HasAVX512BF16 bool // Advanced vector extension 512 BFloat16 Instructions HasBMI1 bool // Bit manipulation instruction set 1 HasBMI2 bool // Bit manipulation instruction set 2 + HasCX16 bool // Compare and exchange 16 Bytes HasERMS bool // Enhanced REP for MOVSB and STOSB HasFMA bool // Fused-multiply-add instructions HasOSXSAVE bool // OS supports XSAVE/XRESTOR for saving/restoring XMM registers. @@ -105,8 +106,8 @@ var ARM64 struct { // ARM contains the supported CPU features of the current ARM (32-bit) platform. // All feature flags are false if: -// 1. the current platform is not arm, or -// 2. the current operating system is not Linux. +// 1. the current platform is not arm, or +// 2. the current operating system is not Linux. var ARM struct { _ CacheLinePad HasSWP bool // SWP instruction support @@ -154,14 +155,13 @@ var MIPS64X struct { // For ppc64/ppc64le, it is safe to check only for ISA level starting on ISA v3.00, // since there are no optional categories. There are some exceptions that also // require kernel support to work (DARN, SCV), so there are feature bits for -// those as well. The minimum processor requirement is POWER8 (ISA 2.07). -// The struct is padded to avoid false sharing. +// those as well. The struct is padded to avoid false sharing. var PPC64 struct { _ CacheLinePad HasDARN bool // Hardware random number generator (requires kernel enablement) HasSCV bool // Syscall vectored (requires kernel enablement) IsPOWER8 bool // ISA v2.07 (POWER8) - IsPOWER9 bool // ISA v3.00 (POWER9) + IsPOWER9 bool // ISA v3.00 (POWER9), implies IsPOWER8 _ CacheLinePad } diff --git a/vendor/golang.org/x/sys/cpu/cpu_aix.go b/vendor/golang.org/x/sys/cpu/cpu_aix.go index 28b5216..8aaeef5 100644 --- a/vendor/golang.org/x/sys/cpu/cpu_aix.go +++ b/vendor/golang.org/x/sys/cpu/cpu_aix.go @@ -20,6 +20,7 @@ func archInit() { PPC64.IsPOWER8 = true } if impl&_IMPL_POWER9 != 0 { + PPC64.IsPOWER8 = true PPC64.IsPOWER9 = true } diff --git a/vendor/golang.org/x/sys/cpu/cpu_arm64.s b/vendor/golang.org/x/sys/cpu/cpu_arm64.s index cfc08c9..c61f95a 100644 --- a/vendor/golang.org/x/sys/cpu/cpu_arm64.s +++ b/vendor/golang.org/x/sys/cpu/cpu_arm64.s @@ -2,6 +2,7 @@ // Use of this source code is governed by a BSD-style // license that can be found in the LICENSE file. +//go:build gc // +build gc #include "textflag.h" diff --git a/vendor/golang.org/x/sys/cpu/cpu_gccgo_x86.go b/vendor/golang.org/x/sys/cpu/cpu_gccgo_x86.go index 8478a6d..863d415 100644 --- a/vendor/golang.org/x/sys/cpu/cpu_gccgo_x86.go +++ b/vendor/golang.org/x/sys/cpu/cpu_gccgo_x86.go @@ -25,3 +25,9 @@ func xgetbv() (eax, edx uint32) { gccgoXgetbv(&a, &d) return a, d } + +// gccgo doesn't build on Darwin, per: +// https://github.com/Homebrew/homebrew-core/blob/HEAD/Formula/gcc.rb#L76 +func darwinSupportsAVX512() bool { + return false +} diff --git a/vendor/golang.org/x/sys/cpu/cpu_loong64.go b/vendor/golang.org/x/sys/cpu/cpu_loong64.go new file mode 100644 index 0000000..0f57b05 --- /dev/null +++ b/vendor/golang.org/x/sys/cpu/cpu_loong64.go @@ -0,0 +1,13 @@ +// Copyright 2022 The Go Authors. All rights reserved. +// Use of this source code is governed by a BSD-style +// license that can be found in the LICENSE file. + +//go:build loong64 +// +build loong64 + +package cpu + +const cacheLineSize = 64 + +func initOptions() { +} diff --git a/vendor/golang.org/x/sys/cpu/cpu_s390x.s b/vendor/golang.org/x/sys/cpu/cpu_s390x.s index 964946d..96f81e2 100644 --- a/vendor/golang.org/x/sys/cpu/cpu_s390x.s +++ b/vendor/golang.org/x/sys/cpu/cpu_s390x.s @@ -2,6 +2,7 @@ // Use of this source code is governed by a BSD-style // license that can be found in the LICENSE file. +//go:build gc // +build gc #include "textflag.h" diff --git a/vendor/golang.org/x/sys/cpu/cpu_x86.go b/vendor/golang.org/x/sys/cpu/cpu_x86.go index fd380c0..f5aacfc 100644 --- a/vendor/golang.org/x/sys/cpu/cpu_x86.go +++ b/vendor/golang.org/x/sys/cpu/cpu_x86.go @@ -39,6 +39,7 @@ func initOptions() { {Name: "avx512bf16", Feature: &X86.HasAVX512BF16}, {Name: "bmi1", Feature: &X86.HasBMI1}, {Name: "bmi2", Feature: &X86.HasBMI2}, + {Name: "cx16", Feature: &X86.HasCX16}, {Name: "erms", Feature: &X86.HasERMS}, {Name: "fma", Feature: &X86.HasFMA}, {Name: "osxsave", Feature: &X86.HasOSXSAVE}, @@ -73,6 +74,7 @@ func archInit() { X86.HasPCLMULQDQ = isSet(1, ecx1) X86.HasSSSE3 = isSet(9, ecx1) X86.HasFMA = isSet(12, ecx1) + X86.HasCX16 = isSet(13, ecx1) X86.HasSSE41 = isSet(19, ecx1) X86.HasSSE42 = isSet(20, ecx1) X86.HasPOPCNT = isSet(23, ecx1) @@ -87,8 +89,15 @@ func archInit() { // Check if XMM and YMM registers have OS support. osSupportsAVX = isSet(1, eax) && isSet(2, eax) - // Check if OPMASK and ZMM registers have OS support. - osSupportsAVX512 = osSupportsAVX && isSet(5, eax) && isSet(6, eax) && isSet(7, eax) + if runtime.GOOS == "darwin" { + // Darwin doesn't save/restore AVX-512 mask registers correctly across signal handlers. + // Since users can't rely on mask register contents, let's not advertise AVX-512 support. + // See issue 49233. + osSupportsAVX512 = false + } else { + // Check if OPMASK and ZMM registers have OS support. + osSupportsAVX512 = osSupportsAVX && isSet(5, eax) && isSet(6, eax) && isSet(7, eax) + } } X86.HasAVX = isSet(28, ecx1) && osSupportsAVX diff --git a/vendor/golang.org/x/sys/cpu/cpu_x86.s b/vendor/golang.org/x/sys/cpu/cpu_x86.s index 2f557a5..39acab2 100644 --- a/vendor/golang.org/x/sys/cpu/cpu_x86.s +++ b/vendor/golang.org/x/sys/cpu/cpu_x86.s @@ -2,6 +2,7 @@ // Use of this source code is governed by a BSD-style // license that can be found in the LICENSE file. +//go:build (386 || amd64 || amd64p32) && gc // +build 386 amd64 amd64p32 // +build gc diff --git a/vendor/golang.org/x/sys/cpu/syscall_aix_gccgo.go b/vendor/golang.org/x/sys/cpu/syscall_aix_gccgo.go index a864f24..9613415 100644 --- a/vendor/golang.org/x/sys/cpu/syscall_aix_gccgo.go +++ b/vendor/golang.org/x/sys/cpu/syscall_aix_gccgo.go @@ -5,7 +5,7 @@ // Recreate a getsystemcfg syscall handler instead of // using the one provided by x/sys/unix to avoid having // the dependency between them. (See golang.org/issue/32102) -// Morever, this file will be used during the building of +// Moreover, this file will be used during the building of // gccgo's libgo and thus must not used a CGo method. //go:build aix && gccgo diff --git a/vendor/modules.txt b/vendor/modules.txt index 95f844f..be5ef87 100644 --- a/vendor/modules.txt +++ b/vendor/modules.txt @@ -1,16 +1,30 @@ -# github.com/btcsuite/btcd v0.21.0-beta +# github.com/btcsuite/btcd v0.22.1 +## explicit; go 1.17 github.com/btcsuite/btcd/btcec github.com/btcsuite/btcd/chaincfg -github.com/btcsuite/btcd/chaincfg/chainhash github.com/btcsuite/btcd/wire +# github.com/btcsuite/btcd/btcec/v2 v2.2.0 +## explicit; go 1.17 +github.com/btcsuite/btcd/btcec/v2 +github.com/btcsuite/btcd/btcec/v2/ecdsa +# github.com/btcsuite/btcd/chaincfg/chainhash v1.0.1 +## explicit; go 1.17 +github.com/btcsuite/btcd/chaincfg/chainhash # github.com/btcsuite/btcutil v1.0.3-0.20201208143702-a53e38424cce +## explicit; go 1.14 github.com/btcsuite/btcutil github.com/btcsuite/btcutil/base58 github.com/btcsuite/btcutil/bech32 github.com/btcsuite/btcutil/hdkeychain # github.com/davecgh/go-spew v1.1.1 +## explicit github.com/davecgh/go-spew/spew -# github.com/ethereum/go-ethereum v1.10.4 +# github.com/decred/dcrd/dcrec/secp256k1/v4 v4.0.1 +## explicit; go 1.16 +github.com/decred/dcrd/dcrec/secp256k1/v4 +github.com/decred/dcrd/dcrec/secp256k1/v4/ecdsa +# github.com/ethereum/go-ethereum v1.10.17 +## explicit; go 1.15 github.com/ethereum/go-ethereum github.com/ethereum/go-ethereum/accounts github.com/ethereum/go-ethereum/common @@ -26,12 +40,16 @@ github.com/ethereum/go-ethereum/crypto/secp256k1/libsecp256k1/src/modules/recove github.com/ethereum/go-ethereum/event github.com/ethereum/go-ethereum/params github.com/ethereum/go-ethereum/rlp -# github.com/tyler-smith/go-bip39 v1.0.1-0.20181017060643-dbb3b84ba2ef +github.com/ethereum/go-ethereum/rlp/internal/rlpstruct +# github.com/tyler-smith/go-bip39 v1.1.0 +## explicit; go 1.14 github.com/tyler-smith/go-bip39 github.com/tyler-smith/go-bip39/wordlists -# golang.org/x/crypto v0.0.0-20210322153248-0c34fe9e7dc2 +# golang.org/x/crypto v0.0.0-20220518034528-6f7dac969898 +## explicit; go 1.17 golang.org/x/crypto/pbkdf2 golang.org/x/crypto/ripemd160 golang.org/x/crypto/sha3 -# golang.org/x/sys v0.0.0-20210420205809-ac73e9fd8988 +# golang.org/x/sys v0.0.0-20220520151302-bc2c85ada10a +## explicit; go 1.17 golang.org/x/sys/cpu