This repository collects the latest research progress of Privacy-Preserving Recommender Systems after 2018.
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Personalized Privacy-Preserving Social Recommendation
Feb. 2018, AAAI'18, [PDF]
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Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy
Feb. 2018, TKDE 2018, [PDF]
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Efficient Privacy-Preserving Matrix Factorization for Recommendation via Fully Homomorphic Encryption
Jun. 2018, Transactions on Privacy and Security 2018, [PDF]
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Privacy-preserving Cross-domain Location Recommendation
Mar. 2019, Proc. ACM IMWUT, [PDF]
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A Privacy-Preserving Distributed Contextual Federated Online Learning Framework with Big Data Support in Social Recommender Systems
Aug. 2019, TKDE, [PDF]
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Decentralized Recommendation Based on Matrix Factorization: A Comparison of Gossip and Federated Learning
Sep. 2019, MLKDD-ECML PKDD'19, [PDF]
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A Simple and Efficient Federated Recommender System
Dec. 2019, BDCAT'19, [PDF]
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Federated Recommendation System via Differential Privacy
May. 2020, ISIT'20, [PDF]
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Meta Matrix Factorization for Federated Rating Predictions
Jun. 2020, SIGIR'20, [PDF]
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DPLCF: Differentially Private Local Collaborative Filtering
Jun. 2020, SIGIR'20, [PDF]
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Federated CF: Privacy-Preserving Collaborative Filtering Cross Multiple Datasets
Jun. 2020, ICC'20, [PDF]
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Privacy Threats Against Federated Matrix Factorization
Jul. 2020, FL-IJCAI'20, [PDF]
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Practical Privacy Preserving POI Recommendation
Jul. 2020, TIST 2020, [PDF]
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A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations
Aug. 2020, FTL-IJCAI'21, [PDF]
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FedFast: Going beyond Average for Faster Training of Federated Recommender Systems
Aug. 2020, KDD'20, [PDF]
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Secure Efficient Federated KNN for Recommendation Systems
Aug. 2020, ICNC-FSKD‘20, [PDF]
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FedRec: Federated Recommendation with Explicit Feedback
Aug. 2020, IEEE Intelligent Systems 2020, [PDF]
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Secure Federated Matrix Factorization
Aug. 2020, IEEE Intelligent Systems 2020, [PDF]
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A Federated Recommender System for Online Services
Sep. 2020, RecSys‘20, [PDF]
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Privacy-Preserving News Recommendation Model Learning
Oct. 2020, EMNLP'20, [PDF]
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Federated Multi-Armed Bandits
Dec. 2020, AAAI'21, [PDF]
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FedRec++: Lossless Federated Recommendation with Explicit Feedback
Dec. 2020, AAAI'21, [PDF]
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FedeRank: User Controlled Feedback with Federated Recommender Systems
Jan. 2021, ECIR'21, [PDF]
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Differentially private locality sensitive hashing based federated recommender system
Feb. 2021, Concurrency and Computation: Practice and Experience, [PDF]
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Personalized Recommendation Algorithm for Mobile Based on Federated Matrix Factorization
Mar. 2021, CDMMS'20, [PDF]
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A Federated Learning Approach for Privacy Protection in Context-Aware Recommender Systems
Apr. 2021, The Computer Journal, [PDF]
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DeepRec: On-device Deep Learning for Privacy-Preserving Sequential Recommendation in Mobile Commerce
Apr. 2021, WWW'21, [PDF]
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Federated Multi-armed Bandits with Personalization
Apr. 2021, AISTATS'21, [PDF]
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DARES: An Asynchronous Distributed Recommender System Using Deep Reinforcement Learning
Jun. 2021, IEEE Access, [PDF]
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Demystifying Model Averaging for Communication-Efficient Federated Matrix Factorization
Jun. 2021, ICASSP'21, [PDF]
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Federated matrix factorization for privacy-preserving recommender systems
Jul. 2021, Applied Soft Computing, [PDF]
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Demystifying Model Averaging for Communication-Efficient Federated Matrix Factorization
Jun. 2021, ICASSP'21, [PDF]
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Learning Federated Representations and Recommendations with Limited Negatives
Aug. 2021, NFFL NIPS'21, [PDF]
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Fast-adapting and Privacy-preserving Federated Recommender System
Sep. 2021, VLDB J 2021, [PDF]
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A Validated Privacy-Utility Preserving Recommendation System with Local Differential Privacy
Sep. 2021, BigDataSE 2021, [PDF]
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Stronger Privacy for Federated Collaborative Filtering with Implicit Feedback
Sep. 2021, RecSys‘21, [PDF]
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Privacy Preserving Collaborative Filtering by Distributed Mediation
Sep. 2021, RecSys‘21, [PDF]
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Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving
Oct. 2021, EMNLP 2021, [PDF]
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Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
Jan. 2019, arXiv, [PDF]
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Federating Recommendations Using Differentially Private Prototypes
Mar. 2020, arXiv, [PDF]
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Privacy-preserving and yet Robust Collaborative Filtering Recommender as a Service
Oct. 2019, arXiv, [PDF]
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FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation
Mar. 2020, arXiv, [PDF]
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Federated Multi-view Matrix Factorization for Personalized Recommendations
Apr. 2020, arXiv, [PDF]
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Survey of Privacy-Preserving Collaborative Filtering
Apr. 2020, arXiv, [PDF]
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Robust Federated Recommendation System
Jun. 2020, arXiv, [PDF]
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Shared MF: A privacy-preserving recommendation system
Aug. 2020, ArXiv, [PDF]
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A Novel Privacy-Preserved Recommender System Framework based on Federated Learning
Nov. 2020, arXiv, [PDF]
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Federated Neural Collaborative Filtering
Jun. 2021, arXiv, [PDF]
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Practical and Secure Federated Recommendation with Personalized Masks
Aug. 2021, arXiv, [PDF]
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PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion
Oct. 2021, arXiv, [PDF]