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Apprenticeship

This part of my life is called "The Pursuit of Doctorate". Here is my blog about learning process.

Diving In

Legend:

📒 📝 ⌨️ 📷 💾
PDF
Files		 Notes Code Slides Other
Supplementaries

Awesome Privacy Protection Research Materials

A curated list of awesome privacy protection research materials.

Courses:

Papers:

🔝

Books

Title Authors Published in Year Files Notes Supplementaries
数据库系统概论 王珊, 萨师煊 高等教育出版社 2014 📝

  • | The Algorithmic Foundations of Differential Privacy | Cynthia Dwork, Aaron Roth | TCS | 2014 | [:ledger:](https://www.nowpublishers.com/article/Details/TCS-042) | | [:floppy_disk:](http://www.cis.upenn.edu/~aaroth/courses/privacyF11.html) |

  • Jordi Soria-Comas, Josep Domingo-Ferrer: Optimal data-independent noise for differential privacy. Inf. Sci. 250: 200-214 (2013)

  • Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu: Differential Privacy and Applications. Advances in Information Security 69, Springer 2017, ISBN 978-3-319-62002-2, pp. 1-222

  • Ninghui Li, Min Lyu, Dong Su, Weining Yang: Differential Privacy: From Theory to Practice. Synthesis Lectures on Information Security, Privacy, & Trust, Morgan & Claypool Publishers 2016, pp. 1-138

🔝

Tutorial

Instructors Institution Year Files Notes Supplementaries
The U.S. Census Bureau Adopts Differential Privacy John M. Abowd U.S. Census Bureau KDD 2018 📒 📷
  • | The Algorithmic Foundations of Data Privacy | Aaron Roth | Penn | Fall 2011 | [:ledger:](http://www.cis.upenn.edu/~aaroth/courses/privacyF11.html) | | [:floppy_disk:](https://www.nowpublishers.com/article/Details/TCS-042) |

🔝

Policy Brief

Title Authors Published in Year Files Notes Supplementaries
China's Social Credit System: A Mark of Progress or a Threat to Privacy? Martin Chorzempa, Paul Triolo, Samm Sacks 2018 📒 / 💾

🔝

Survey

Title Authors Published in Year Files Notes Supplementaries
Differentially Private Data Publishing and Analysis: A Survey Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu TKDE 2017 📒
Privacy for Recommender Systems: Tutorial Abstract Bart P. Knijnenburg, Shlomo Berkovsky RecSys 2017 📒 📷
Privacy in Location-Based Services: State-of-the-Art and Research Directions Mohamed F. Mokbel MDM 2007 📒 📷

🔝

Basic Techniques

Title Authors Published in Year Files Notes Supplementaries
Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms Fengxiang He, Bohan Wang, Dacheng Tao UAI 2021 📒 📝
OSDP One-sided Differential Privacy Ios Kotsogiannis, Stelios Doudalis, Samuel Haney, Ashwin Machanavajjhala, Sharad Mehrotra ICDE 2020 📒 📷📷
SVT-S Understanding the Sparse Vector Technique for Differential Privacy Min Lyu, Dong Su, Ninghui Li VLDB 2017 📒
Nearly-Optimal Private LASSO Kunal Talwar, Abhradeep Thakurta, Li Zhang NIPS 2015 📒 📝 📒
Privacy-preserving statistical estimation with optimal convergence rates Adam D. Smith STOC 2011 📒
Smooth sensitivity and sampling in private data analysis Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith STOC 2007 📒

🔝

Private Framework

Title Authors Published in Year Files Notes Supplementaries
KTELO KTELO: A Framework for Defining Differentially-Private Computations Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau SIGMOD 2018 📒 ⌨️
  • PINQ: Frank McSherry: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. SIGMOD Conference 2009: 19-30; Davide Proserpio, Sharon Goldberg, Frank McSherry: Calibrating Data to Sensitivity in Private Data Analysis. PVLDB 7(8): 637-648 (2014)
  • Fuzz: Marco Gaboardi, Andreas Haeberlen, Justin Hsu, Arjun Narayan, Benjamin C. Pierce: Linear dependent types for differential privacy. POPL 2013: 357-370
  • PrivInfer: Gilles Barthe, Gian Pietro Farina, Marco Gaboardi, Emilio Jesús Gallego Arias, Andy Gordon, Justin Hsu, Pierre-Yves Strub: Differentially Private Bayesian Programming. ACM Conference on Computer and Communications Security 2016: 68-79
  • LightDP: Danfeng Zhang, Daniel Kifer: LightDP: towards automating differential privacy proofs. POPL 2017: 888-901

🔝

Private Benchmark

  • DPBench: Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, Dan Zhang: Principled Evaluation of Differentially Private Algorithms using DPBench. SIGMOD Conference 2016: 139-154

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Private Data Publishing

  • | | Differentially private data publishing for data analysis | Dong Su | | 2016 | [:ledger:](https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=2220&context=open_access_dissertations) | | |
  • | JTree | Differentially Private High-Dimensional Data Publication via Sampling-Based Inference | Rui Chen, Qian Xiao, Yu Zhang, Jianliang Xu | KDD | 2015 | [:ledger:](https://www.comp.hkbu.edu.hk/~xujl/Papers/kdd15.pdf) | | |
  • | NoisyCut | Top-k frequent itemsets via differentially private FP-trees | Jaewoo Lee, Christopher W. Clifton | KDD | 2014 | [:ledger:](https://cybersecurity.uga.edu/publications/VI_KDD2014.pdf) | | |
  • | PTT<br>k-RecursiveMedians | Differentially Private Algorithms for Empirical Machine Learning | Ben Stoddard, Yan Chen, Ashwin Machanavajjhala | CoRR | 2014 | [:ledger:](https://arxiv.org/pdf/1411.5428.pdf) | | |
Transaction Data Publishing
Title Authors Published in Year Files Notes Supplementaries
PATE-GAN PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees James Jordon, Jinsung Yoon, Mihaela van der Schaar ICLR 2019 📒
Liancheng Privacy as a Service: Publishing Data and Models Ashish Dandekar, Debabrota Basu, Thomas Kister, Geong Sen Poh, Jia Xu, Stéphane Bressan DASFAA 2019 📒
A Data Publishing System Based on Privacy Preservation Zhihui Wang, Yun Zhu, Xuchen Zhou DASFAA 2019 📒
Approximate Query Processing using Deep Generative Models Saravanan Thirumuruganathan, Shohedul Hasan, Nick Koudas, Gautam Das CoRR 2019 📒
G-PATE Scalable Differentially Private Generative Student Model via PATE Yunhui Long, Suxin Lin, Zhuolin Yang, Carl A. Gunter, Bo Li CoRR 2019 📒
DP-GAN-DNN POSTER: A Unified Framework of Differentially Private Synthetic Data Release with Generative Adversarial Network Pei-Hsuan Lu, Chia-Mu Yu CCS 2017 📒 ⌨️
PrivBayes PrivBayes: Private Data Release via Bayesian Networks Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, Xiaokui Xiao TODS
SIGMOD		 2017
2014		 📒
📒		 ⌨️
📷📷
Efficient privacy-preserving temporal and spacial data aggregation for smart grid communications Xiaolei Dong, Jun Zhou, Zhenfu Cao Concurrency 2016 📒 📝

🔝

Streaming Data Publishing
Title Authors Published in Year Files Notes Supplementaries
PeGaSus PeGaSus: Data-Adaptive Differentially Private Stream Processing Yan Chen, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau CCS 2017 📒 📝
  • | CCDPSD | 异方差加噪下差分隐私流数据发布一致性优化算法 | 孙岚, 康健, 吴英杰, 张立群 | 清华大学学报 | 2018 | [:ledger:](http://kns.cnki.net/KCMS/detail/11.2223.N.20180921.0900.001.html) | | |
  • | | 面向实时数据流的差分隐私直方图发布技术 | 杨庚, 夏春婷, 白云璐 | 南京邮电大学学报 | 2018 | [:ledger:](http://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CJFDLAST2018&filename=NJYD201802014) | | |
  • | PrivTree | PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions | Jun Zhang, Xiaokui Xiao, Xing Xie | SIGMOD | 2016 | [:ledger:](http://delivery.acm.org/10.1145/2890000/2882928/p155-zhang.pdf) | | |

🔝

Graph Data Publishing
Title Authors Published in Year Files Notes Supplementaries
GSN-DP Geo-social network publication based on differential privacy Xiaochun Wang, Yidong Li FCS 2018 📒 📝 📷

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Image Data Publishing
Title Authors Published in Year Files Notes Supplementaries
RPM Random permutation Maxout transform for cancellable facial template protection Andrew Beng Jin Teoh, Sejung Cho, Jihyeon Kim MTA 2018 📒 📝
BEMK 面向人脸图像发布的差分隐私保护 张啸剑, 付聪聪, 孟小峰 JIG 2018 📒 📝
DPGAN Differentially Private Generative Adversarial Network Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, Jiayu Zhou CoRR 2018 📒 ⌨️

🔝

Private Data Analysis

Private Learning
Title Authors Published in Year Files Notes Supplementaries
Robust anomaly detection and backdoor attack detection via differential privacy Min Du, Ruoxi Jia, Dawn Song ICLR 2020 📒 📝⌨️
Differentially Private Meta-Learning Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar ICLR 2020 📒 📝
Privacy Enhanced Multimodal Neural Representations for Emotion Recognition Mimansa Jaiswal, Emily Mower Provost AAAI 2020 📒
Utility/Privacy Trade-off through the lens of Optimal Transport Etienne Boursier, Vianney Perchet AISTATS 2020 📒 ⌨️
Understanding Gradient Clipping in Private SGD: A Geometric Perspective Xiangyi Chen, Zhiwei Steven Wu, Mingyi Hong NeurIPS 2020 📒 📝📝📝 📒
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy Kareem Amin, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii ICML 2019 📒 📝📷📷
A General Approach to Adding Differential Privacy to Iterative Training Procedures H. Brendan McMahan, Galen Andrew CoRR 2018 📒 ⌨️
AdaClip AdaCliP: Adaptive Clipping for Private SGD Venkatadheeraj Pichapati, Ananda Theertha Suresh, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar CoRR 2019 📒
Differentially Private Learning with Adaptive Clipping Om Thakkar, Galen Andrew, H. Brendan McMahan CoRR 2019 📒
DP-FedAvg Learning Differentially Private Recurrent Language Models H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang ICLR 2018 📒 📝
Three Tools for Practical Differential Privacy Koen Lennart van der Veen, Ruben Seggers, Peter Bloem, Giorgio Patrini PPML 2018 📒
dp-GAN Differentially Private Releasing via Deep Generative Model Xinyang Zhang, Shouling Ji, Ting Wang CoRR 2018 📒 ⌨️
Differentially Private Federated Learning: A Client Level Perspective Robin C. Geyer, Tassilo Klein, Moin Nabi CoRR 2017 📒 ⌨️
💾
DPSGD Deep Learning with Differential Privacy Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang CCS 2016 📒 💾
⌨️

🔝

Frequent Itemset Mining
Title Authors Published in Year Files Notes Supplementaries
Diff-FPM Mining frequent graph patterns with differential privacy Entong Shen, Ting Yu KDD 2013 📒 📷
  • | PrivBasis | PrivBasis: Frequent Itemset Mining with Differential Privacy | Ninghui Li, Wahbeh H. Qardaji, Dong Su, Jianneng Cao | PVLDB | 2012 | [:ledger:](https://dl.acm.org/citation.cfm?id=2350251) | | [:keyboard:](https://github.com/DongSuIBM/PrivBasis) |

🔝

Graph Data Analysis
Title Authors Published in Year Files Notes Supplementaries
FedGNN FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation Chuhan Wu, Fangzhao Wu, Yang Cao, Yongfeng Huang, Xing Xie CoRR 2021 📒
Adam-DP Privacy-Preserving Graph Convolutional Networks for Text Classification Timour Igamberdiev, Ivan Habernal CoRR 2021 📒
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha ICML 2020 📒 📒
PPKG Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications Chaochao Chen, Jamie Cui, Guanfeng Liu, Jia Wu, Li Wang CoRR 2020 📒
APGE Adversarial Privacy Preserving Graph Embedding against Inference Attack Kaiyang Li, Guangchun Luo, Yang Ye, Wei Li, Shihao Ji, Zhipeng Cai CoRR 2020 📒 ⌨️
LPGNN Locally Private Graph Neural Networks Sina Sajadmanesh, Daniel Gatica-Perez CoRR 2020 📒 ⌨️

🔝

Recommender Systems
Title Authors Published in Year Files Notes Supplementaries
Extended PrivSR Towards privacy preserving social recommendation under personalized privacy settings Xuying Meng, Suhang Wang, Kai Shu, Jundong Li, Bo Chen, Huan Liu, Yujun Zhang WWWJ 2019 📒
FedMF Secure Federated Matrix Factorization Di Chai, Leye Wang, Kai Chen, Qiang Yang FML 2019 📒 📝 ⌨️
GD-DR Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy Hyejin Shin, Sungwook Kim, Junbum Shin, Xiaokui Xiao TKDE 2018 📒 📝
PrivSR Personalized Privacy-Preserving Social Recommendation Xuying Meng, Suhang Wang, Kai Shu, Jundong Li, Bo Chen, Huan Liu, Yujun Zhang AAAI 2018 📒 📝 💾
DMF Privacy Preserving Point-of-Interest Recommendation Using Decentralized Matrix Factorization Chaochao Chen, Ziqi Liu, Peilin Zhao, Jun Zhou, Xiaolong Li AAAI 2018 📒 📝
EpicRec EpicRec: Towards Practical Differentially Private Framework for Personalized Recommendation Yilin Shen, Hongxia Jin CCS 2016 📒
DPMF Differentially Private Matrix Factorization Jingyu Hua, Chang Xia, Sheng Zhong IJCAI 2015 📒 📝
DP-UnP3R Privacy-Preserving Personalized Recommendation: An Instance-Based Approach via Differential Privacy Yilin Shen, Hongxia Jin ICDM 2014 📒

🔝

Survival Analysis
Title Authors Published in Year Files Notes Supplementaries
Differentially Private Survival Function Estimation Lovedeep Gondara, Ke Wang MLHC 2020 📒 ⌨️

🔝

Natural Language Processing
Title Authors Published in Year Files Notes Supplementaries
Information Leakage in Embedding Models Congzheng Song, Ananth Raghunathan CCS 2020 📒 📷⌨️
Privacy Risks of General-Purpose Language Models Xudong Pan, Mi Zhang, Shouling Ji, Min Yang IEEE Symposium on Security and Privacy 📒
DPNR Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness Lingjuan Lyu, Xuanli He, Yitong Li EMNLP 2020 📒 ⌨️
TextHide TextHide: Tackling Data Privacy for Language Understanding Tasks Yangsibo Huang, Zhao Song, Danqi Chen, Kai Li, Sanjeev Arora EMNLP 2020 📒 ⌨️
PolicyQA PolicyQA: A Reading Comprehension Dataset for Privacy Policies Wasi Uddin Ahmad, Jianfeng Chi, Yuan Tian, Kai-Wei Chang EMNLP 2020 📒 ⌨️
FedNewsRec Privacy-Preserving News Recommendation Model Learning Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie EMNLP 2020 📒 ⌨️
MG-PriFair Multimodal Review Generation with Privacy and Fairness Awareness Xuan-Son Vu, Thanh-Son Nguyen, Duc-Trong Le, Lili Jiang COLING 2020 📒 📷
Towards Privacy by Design in Learner Corpora Research: A Case of On-the-fly Pseudonymization of Swedish Learner Essays Elena Volodina, Yousuf Ali Mohammed, Sandra Derbring, Arild Matsson, Beáta Megyesi COLING 2020 📒
OME Towards Differentially Private Text Representations Lingjuan Lyu, Yitong Li, Xuanli He, Tong Xiao SIGIR 2020 📒 📝 ⌨️

🔝

Machine Unlearning

Title Authors Published in Year Files Notes Supplementaries
When Machine Unlearning Jeopardizes Privacy Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, Yang Zhang CCS 2021 📒 ⌨️📷
Mixed-Linear Forgetting Mixed-Privacy Forgetting in Deep Networks Aditya Golatkar, Alessandro Achille, Avinash Ravichandran, Marzia Polito, Stefano Soatto CVPR 2021 📒 📒
Remember What You Want to Forget: Algorithms for Machine Unlearning Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh CoRR 2021 📒
GraphEraser Graph Unlearning Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, Yang Zhang CoRR 2021 📒
DeltaGrad DeltaGrad: Rapid retraining of machine learning models Yinjun Wu, Edgar Dobriban, Susan B. Davidson ICML 2020 📒 📒📷📷⌨️
Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks Aditya Golatkar, Alessandro Achille, Stefano Soatto CVPR 2020 📒 📒📷⌨️
Towards Probabilistic Verification of Machine Unlearning David Marco Sommer, Liwei Song, Sameer Wagh, Prateek Mittal CoRR 2020 📒 ⌨️
Verifying that the influence of a user data point has been removed from a machine learning classifier Saurabh Shintre, Jasjeet Dhaliwal Patent 2018 📒

🔝

Cryptography

Oblivious Transfer
Title Authors Published in Year Files Notes Supplementaries
Improved Private Set Intersection Against Malicious Adversaries Peter Rindal, Mike Rosulek EUROCRYPT 2017 📒 ⌨️

🔝

Searchable Encryption
Title Authors Published in Year Files Notes Supplementaries
SPiRiT Secure Search on Encrypted Data via Multi-Ring Sketch Adi Akavia, Dan Feldman, Hayim Shaul CCS 2018 📒 📝
CPABKS Secure and Efficient Attribute-Based Encryption with Keyword Search Haijiang Wang, Xiaolei Dong, Zhenfu Cao, Dongmei Li CJ 2018 📒 📝

🔝

Encrypted Database
Title Authors Published in Year Files Notes Supplementaries
CryptZip Sa Wang, Yiwen Shao, Yungang Bao Practices of backuping homomorphically encrypted databases FCS 2019 📒 📷
一种基于保形加密的大数据脱敏系统实现及评估 卞超轶, 朱少敏, 周涛 电信科学 2017 📒

🔝

Encrypted Retrieval
Title Authors Published in Year Files Notes Supplementaries
Practical Approximate k Nearest Neighbor Queries with Location and Query Privacy Xun Yi, Russell Paulet, Elisa Bertino, Vijay Varadharajan TKDE 2016 📒
A Privacy-Preserving Framework for Large-Scale Content-Based Information Retrieval Li Weng, Laurent Amsaleg, April Morton, Stéphane Marchand-Maillet TIFS 2015 📒
Privacy-Preserving and Content-Protecting Location Based Queries Russell Paulet, Md. Golam Kaosar, Xun Yi, Elisa Bertino TKDE 2014 📒

🔝

Encrypted Inference
Title Authors Published in Year Files Notes Supplementaries
CryptoNets CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin E. Lauter, Michael Naehrig, John Wernsing ICML 2016 📒

🔝

Awesome Federated Learning Research Materials

A curated list of awesome federated learning research materials.

Courses:

Papers:

🔝

Tutorial

Instructors Institution Year Files Notes Supplementaries
GDPR, Data Shortage and AI Qiang Yang HKUST 2019 📷

🔝

Survey

Title Authors Published in Year Files Notes Supplementaries
Federated Machine Learning: Concept and Applications Qiang Yang, Yang Liu, Tianjian Chen, Yongxin Tong TIST 2019 📒 📝 💾
Federated Learning Florian Hartmann 2018 📒 ⌨️⌨️⌨️

🔝

Algorithm

Communication
Title Authors Published in Year Files Notes Supplementaries
Efficient and Robust Asynchronous Federated Learning with Stragglers Ming Chen, Bingcheng Mao, Tianyi Ma CoRR 2020 📒

🔝

Aggregation
Title Authors Published in Year Files Notes Supplementaries
q-FedAvg Fair Resource Allocation in Federated Learning Tian Li, Maziar Sanjabi, Virginia Smith ICLR 2020 📒
AFL Agnostic Federated Learning Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh ICML 2019 📒 💾
📷
FedAvg Communication-Efficient Learning of Deep Networks from Decentralized Data Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas AISTATS 2017 📒 💾

🔝

Application
Title Authors Published in Year Files Notes Supplementaries
Federated CIFG Federated Learning for Mobile Keyboard Prediction Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, Daniel Ramage CoRR 2018 📒 💾
Federated Meta-Learning for Recommendation Fei Chen, Zhenhua Dong, Zhenguo Li, Xiuqiang He CoRR 2018 📒

🔝

System

Title Authors Published in Year Files Notes Supplementaries
Towards Federated Learning at Scale: System Design Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander SysML 2019 📒

🔝

Awesome Fairness Research Materials

A curated list of awesome fairness research materials.

Courses:

Papers:

🔝

Tutorial

Instructors Institution Year Files Notes Supplementaries
Fairness and Control of Exposure in Two-sided Markets Thorsten Joachims ICTIR 2021 📒
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned Sarah Bird, Ben Hutchinson, Krishnaram Kenthapadi, Emre Kıcıman, Margaret Mitchell Microsoft
Google
LinkedIn		 KDD 2019 📝 📷
Challenges of incorporating algorithmic fairness into industry practice Henriette Cramer, Ken Holstein, Jenn Wortman Vaughan, Hal Daumé III, Miroslav Dudík, Hanna Wallach, Sravana Reddy, Jean Garcia-Gathright Microsoft Research ACM FAccT 2019 📷

🔝

Causal Fairness

Instructors Institution Year Files Notes Supplementaries
Counterfactual reasoning in algorithmic fairness Ricardo Silva UCL
Alan Turing Institute		 FairWare @ ICSE 2018 📷
Fairness in Machine Learning and Its Causal Aspects Ricardo Silva UCL
Alan Turing Institute		 2017 📷

🔝

Survey

Title Authors Published in Year Files Notes Supplementaries
Fairness-Aware Recommendation in Multi-Sided Platforms Masoud Mansoury WSDM 2021 📒
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness Jessie Finocchiaro, Roland Maio, Faidra Monachou, Gourab K. Patro, Manish Raghavan, Ana-Andreea Stoica, Stratis Tsirtsis CoRR 2020 📒
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, Aram Galstyan CoRR 2019 📒
The Frontiers of Fairness in Machine Learning Alexandra Chouldechova, Aaron Roth CoRR 2018 📒

🔝

Statistical Fairness

Title Authors Published in Year Files Notes Supplementaries
EquiTensors EquiTensors: Learning Fair Integrations of Heterogeneous Urban Data An Yan, Bill Howe SIGMOD 2021 📒 📷⌨️
Constructing a Fair Classifier with Generated Fair Data Taeuk Jang, Feng Zheng, Xiaoqian Wang AAAI 2021 📒
CFair Conditional Learning of Fair Representations Han Zhao, Amanda Coston, Tameem Adel, Geoffrey J. Gordon ICLR 2020 📒 📝 📝
Fairness warnings
fair-MAML		 Fairness warnings and fair-MAML: learning fairly with minimal data Dylan Slack, Sorelle A. Friedler, Emile Givental FAT* 2020 📒 📝📷📷⌨️
AdvDebias Inherent Tradeoffs in Learning Fair Representations Han Zhao, Geoffrey J. Gordon NeurIPS 2019 📒 📝 📝📝📝📷📷⌨️
Random Repair Obtaining Fairness using Optimal Transport Theory Paula Gordaliza, Eustasio del Barrio, Fabrice Gamboa, Jean-Michel Loubes ICML 2019 📒 📒⌨️📷
FFVAE Flexibly Fair Representation Learning by Disentanglement Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel ICML 2019 📒 📒📷
DP-postprocessing
DP-oracle-learner		 Differentially Private Fair Learning Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman ICML 2019 📒 📒📷
Fair Regression Fair Regression: Quantitative Definitions and Reduction-Based Algorithms Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu ICML 2019 📒 📒⌨️⌨️📷
Pairwise Fairness Fairness in Recommendation Ranking through Pairwise Comparisons Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow KDD 2019 📒 📝 📷
Strong Demographic Parity Wasserstein Fair Classification Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa UAI 2019 📒 📒⌨️
Exploring Human Gender Stereotypes with Word Association Test Yupei Du, Yuanbin Wu, Man Lan EMNLP 2019 📒 ⌨️
AVD Penalizers
SD Penalizers		 Penalizing Unfairness in Binary Classification Yahav Bechavod, Katrina Ligett CoRR 2017 📒 ⌨️
Equalized Odds Equality of Opportunity in Supervised Learning Moritz Hardt, Eric Price, Nati Srebro NIPS 2016 📒 📒📝

🔝

Causal Fairness

Title Authors Published in Year Files Notes Supplementaries
Counterfactual Privilege Making Decisions that Reduce Discriminatory Impacts Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva ICML 2019 📒 📒⌨️
📷📷
Fair K
Fair Add		 Counterfactual Fairness Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva NIPS 2017 📒 📝 📒⌨️📷📷
Multi-World Fairness When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness Chris Russell, Matt J. Kusner, Joshua R. Loftus, Ricardo Silva NIPS 2017 📒 📝 📷

🔝

Resource Allocation

Title Authors Published in Year Files Notes Supplementaries
Slice Tuner Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning Models SIGMOD 2021 📒 📷📷⌨️
TFROM TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers Yao Wu, Jian Cao, Guandong Xu, Yudong Tan SIGIR 2021 📒 📷
FairBatch FairBatch: Batch Selection for Model Fairness Yuji Roh, Kangwook Lee, Steven Euijong Whang, Changho Suh ICLR 2021 📒 📝 📷📷⌨️
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang FAccT 2021 📒
TSFD Rank User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets Lequn Wang, Thorsten Joachims ICTIR 2021 📒
EARS Top-K Contextual Bandits with Equity of Exposure Olivier Jeunen, Bart Goethals RecSys 2021 📒 ⌨️📷
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H. Chi, Jilin Chen, Alex Beutel AIES 2021 📒
ARL Fairness without Demographics through Adversarially Reweighted Learning Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed Chi NeurIPS 2020 📒 📝📝📝📒⌨️
FairCo Controlling Fairness and Bias in Dynamic Learning-to-Rank Marco Morik, Ashudeep Singh, Jessica Hong, Thorsten Joachims SIGIR 2020 📒 📝📷⌨️
FairRec FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms Gourab K. Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, Abhijnan Chakraborty WWW 2020 📒 ⌨️📷📷
Fair Updates in Two-Sided Market Platforms: On Incrementally Updating Recommendations Gourab K. Patro, Abhijnan Chakraborty, Niloy Ganguly, Krishna P. Gummadi AAAI 2020 📒 📷
Equalized odds postprocessing under imperfect group information Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern AISTATS 2020 📒 📒⌨️
Fair decision making using privacy-protected data David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau FAT* 2020 📒 📷📷
DPSGD-F Removing Disparate Impact of Differentially Private Stochastic Gradient Descent on Model Accuracy Depeng Xu, Wei Du, Xintao Wu CoRR 2020 📒
FEN Learning Fairness in Multi-Agent Systems Jiechuan Jiang, Zongqing Lu NeurIPS 2019 📒 📒📝📝📝⌨️
Differential Privacy Has Disparate Impact on Model Accuracy Eugene Bagdasaryan, Omid Poursaeed, Vitaly Shmatikov NeurIPS 2019 📒 📝📝📝📷⌨️
DC
Maximin		 Group-Fairness in Influence Maximization Alan Tsang, Bryan Wilder, Eric Rice, Milind Tambe, Yair Zick IJCAI 2019 📒 📝⌨️
TREE01 Fairness without Harm: Decoupled Classifiers with Preference Guarantees Berk Ustun, Yang Liu, David C. Parkes ICML 2019 📒 📒⌨️📷
Greedy Capture
Local Capture		 Proportionally Fair Clustering Xingyu Chen, Brandon Fain, Liang Lyu, Kamesh Munagala ICML 2019 📒 📝⌨️📷
GF1A/B Group Fairness for the Allocation of Indivisible Goods Vincent Conitzer, Rupert Freeman, Nisarg Shah, Jennifer Wortman Vaughan AAAI 2019 📒 📝
PCCS
TFCS		 Fair Transfer Learning with Missing Protected Attributes Amanda Coston, Karthikeyan Natesan Ramamurthy, Dennis Wei, Kush R. Varshney, Skyler Speakman, Zairah Mustahsan, Supriyo Chakraborty AIES 2019 📒
FULTR Fair Learning-to-Rank from Implicit Feedback Himank Yadav, Zhengxiao Du, Thorsten Joachims CoRR 2019 📒
Fairness Without Demographics in Repeated Loss Minimization Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang ICML 2018 📒 📒⌨️

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Others

Stability
Title Authors Published in Year Files Notes Supplementaries
Stable-Fair Stable and Fair Classification Lingxiao Huang, Nisheeth K. Vishnoi ICML 2019 📒 📝

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Awesome Blockchain Research Materials

A curated list of awesome blockchain research materials.

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Survey

Title Authors Published in Year Files Notes Supplementaries
Yellow Paper Ethereum: A Secure Decentralised Generalised Transaction Ledger Dr. Gavin Wood 📒
黄皮书 以太坊：一种安全去中心化的通用交易账本 崔广斌, 高天露 📒
Untangling Blockchain: A Data Processing View of Blockchain Systems Tien Tuan Anh Dinh, Rui Liu, Meihui Zhang, Gang Chen, Beng Chin Ooi, Ji Wang TKDE 2018 📒 📝 💾
Making Sense of Blockchain Applications: A Typology for HCI Chris Elsden, Arthi Manohar, Jo Briggs, Mike Harding, Chris Speed, John Vines CHI 2018 📒 📝
BigchainDB BigchainDB 2.0: The Blockchain Database BigchainDB BigchainDB 2018 📒 ⌨️
BLOCKBENCH BLOCKBENCH: A Framework for Analyzing Private Blockchains Tien Tuan Anh Dinh, Ji Wang, Gang Chen, Rui Liu, Beng Chin Ooi, Kian-Lee Tan SIGMOD 2017 📒 ⌨️
区块链隐私保护研究综述 祝烈煌, 高峰, 沈蒙, 李艳东, 郑宝昆, 毛洪亮, 吴震 计算机研究与发展 2017 📒 📝

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Network Layer

Title Authors Published in Year Files Notes Supplementaries
BlockFL On-Device Federated Learning via Blockchain and its Latency Analysis Hyesung Kim, Jihong Park, Mehdi Bennis, Seong-Lyun Kim CoRR 2018 📒

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Other Awesome Research Materials

A curated list of awesome research materials.

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Artificial Intelligence

Title Authors Published in Year Files Notes Supplementaries
WebFace260M WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, Junjie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Jiwen Lu, Dalong Du, Jie Zhou CVPR 2021 📒 💾
Trustworthy AI Jeannette M. Wing Commun. ACM 2021 📒 📷
细节决定成败：推荐系统实验反思与讨论 施韶韵, 王晨阳, 马为之, 张敏, 刘奕群, 马少平 信息安全学报 2021 📒
Meta-Learning in Neural Networks: A Survey Timothy M. Hospedales, Antreas Antoniou, Paul Micaelli, Amos J. Storkey CoRR 2020 📒 📝
AI Governance in 2019 a Year in Review Qian Shi, Hui Li, Brian Tse, John Hopcroft, Stuart Russell, Caroline Jeanmaire, Qiang Yang, Pascale Fung, Roman Yampolskiy, Allan Dafoe, Markus Anderljung, Gillian K. Hadfield, Jun Su, Thilo Hagendorff, Petra Ahrweiler, Robin Williams, Colin Allen, Poon King Wang, Ferran Jarabo Carbonell, Xiaohong Wang, Qingfeng Yang, Qi Yin, Don Wright, Miles Brundage, Jack Clark, Irene Solaiman, Gretchen Krueger, Seán Ó hÉigeartaigh, Helen Toner, Millie Liu, Steve Hoffman, Irakli Beridze, Wendell Wallach, Cyrus Hodes, Nicolas Miailhe, Jessica Cussins Newman, Dingding Chen, Eva Kaili, Francesca Rossi, Charlotte Stix, Angela Daly, Danit Gal, Arisa Ema, Goh Yihan, Nydia Remolina, Urvashi Aneja, Ying Fu, Zhiyun Zhao, Xiuquan Li, Weiwen Duan, Qun Luan, Rui Guo, Yingchun Wang Shanghai Institute for Science of Science 2020 📒 📝
Meta-Weight-Net Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, Deyu Meng NeurIPS 2019 📒 📝📝📝 📒⌨️
Graph Neural Networks for Natural Language Processing Shikhar Vashishth, Naganand Yadati, Partha Talukdar EMNLP 2019 📷⌨️📷📷
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta, Natalia Díaz Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, Francisco Herrera CoRR 2019 📒 📝
以机器学习的视角来看时序点过程的最新进展 严骏驰 中国自动化学会模式识别与机器智能专业委员会通讯 2019 📒
Temporal Point Processes and the Conditional Intensity Function Jakob Gulddahl Rasmussen CoRR 2018 📒
softImpute-ALS Matrix completion and low-rank SVD via fast alternating least squares Trevor Hastie, Rahul Mazumder, Jason D. Lee, Reza Zadeh JMLR 2015 📒 ⌨️⌨️⌨️
Efficient Per-Example Gradient Computations Ian J. Goodfellow CoRR 2015 📒
RBO A similarity measure for indefinite rankings William Webber, Alistair Moffat, Justin Zobel TOIS 2010 📒
BPR BPR: Bayesian Personalized Ranking from Implicit Feedback Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme UAI 2009 📒
Damped Newton Algorithms for Matrix Factorization with Missing Data A. M. Buchanan, Andrew W. Fitzgibbon CVPR 2005 📒

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Robust Statistics

Title Authors Published in Year Files Notes Supplementaries
Influence Functions in Deep Learning Are Fragile Samyadeep Basu, Phillip Pope, Soheil Feizi ICLR 2021 📒 📝 📷
Group Influence Functions On Second-Order Group Influence Functions for Black-Box Predictions Samyadeep Basu, Xuchen You, Soheil Feizi ICML 2020 📒 📒
TracIn Estimating Training Data Influence by Tracing Gradient Descent Garima Pruthi, Frederick Liu, Satyen Kale, Mukund Sundararajan NeurIPS 2020 📒 📝📝📝📝📝 📒⌨️
On the Accuracy of Influence Functions for Measuring Group Effects Pang Wei Koh, Kai-Siang Ang, Hubert H. K. Teo, Percy Liang NeurIPS 2019 📒 📝📝📝 📒⌨️⌨️📷
Representer Values Representer Point Selection for Explaining Deep Neural Networks Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar NeurIPS 2018 📒 📝📝 📒📷⌨️
Understanding Black-box Predictions via Influence Functions Pang Wei Koh, Percy Liang ICML 2017 📒 📒⌨️⌨️📷

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Generalization

Title Authors Published in Year Files Notes Supplementaries
When is memorization of irrelevant training data necessary for high-accuracy learning? Gavin Brown, Mark Bun, Vitaly Feldman, Adam Smith, Kunal Talwar STOC 2021 📒
Understanding deep learning (still) requires rethinking generalization Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals CACM 2021 📒 📝 📷
Does Learning Require Memorization? A Short Tale about a Long Tail Vitaly Feldman STOC 2020 📒 📒📷📷📷
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation Vitaly Feldman, Chiyuan Zhang NeurIPS 2020 📒 📒📝📝📝⌨️
Understanding deep learning requires rethinking generalization Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals ICLR 2017 📒 📝⌨️📷📷

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Large-Scale Optimization

Title Authors Published in Year Files Notes Supplementaries
BDA A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton Risheng Liu, Pan Mu, Xiaoming Yuan, Shangzhi Zeng, Jin Zhang ICML 2020 📒
AGD+ On Acceleration with Noise-Corrupted Gradients Michael Cohen, Jelena Diakonikolas, Lorenzo Orecchia ICML 2018 📒 💾
  • Olivier Devolder, François Glineur, Yurii Nesterov: First-order methods of smooth convex optimization with inexact oracle. Math. Program. 146(1-2): 37-75 (2014)
  • Alexandre d'Aspremont: Smooth Optimization with Approximate Gradient. SIAM Journal on Optimization 19(3): 1171-1183 (2008)

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Combinatorial Optimization

Title Authors Published in Year Files Notes Supplementaries
GSLS Optimizing top-k retrieval: submodularity analysis and search strategies Chaofeng Sha, Keqiang Wang, Dell Zhang, Xiaoling Wang, Aoying Zhou FCS
WAIM		 2016
2014		 📒
📒
SubmEP Ensemble Pruning: A Submodular Function Maximization Perspective Chaofeng Sha, Keqiang Wang, Xiaoling Wang, Aoying Zhou DASFAA 2014 📒

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