Awesome Privacy-Preserving RS Paper

This repository collects the latest research progress of Privacy-Preserving Recommender Systems after 2018.

Paper List

1. Personalized Privacy-Preserving Social Recommendation

   Feb. 2018, AAAI'18, [PDF]

2. Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy

   Feb. 2018, TKDE 2018, [PDF]

3. Efficient Privacy-Preserving Matrix Factorization for Recommendation via Fully Homomorphic Encryption

   Jun. 2018, Transactions on Privacy and Security 2018, [PDF]

4. Privacy-preserving Cross-domain Location Recommendation

   Mar. 2019, Proc. ACM IMWUT, [PDF]

5. A Privacy-Preserving Distributed Contextual Federated Online Learning Framework with Big Data Support in Social Recommender Systems

   Aug. 2019, TKDE, [PDF]

6. Decentralized Recommendation Based on Matrix Factorization: A Comparison of Gossip and Federated Learning

   Sep. 2019, MLKDD-ECML PKDD'19, [PDF]

7. A Simple and Efficient Federated Recommender System

   Dec. 2019, BDCAT'19, [PDF]

8. Federated Recommendation System via Differential Privacy

   May. 2020, ISIT'20, [PDF]

9. Meta Matrix Factorization for Federated Rating Predictions

   Jun. 2020, SIGIR'20, [PDF]

10. DPLCF: Differentially Private Local Collaborative Filtering

    Jun. 2020, SIGIR'20, [PDF]

11. Federated CF: Privacy-Preserving Collaborative Filtering Cross Multiple Datasets

    Jun. 2020, ICC'20, [PDF]

12. Privacy Threats Against Federated Matrix Factorization

    Jul. 2020, FL-IJCAI'20, [PDF]

13. Practical Privacy Preserving POI Recommendation

    Jul. 2020, TIST 2020, [PDF]

14. A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations

    Aug. 2020, FTL-IJCAI'21, [PDF]

15. FedFast: Going beyond Average for Faster Training of Federated Recommender Systems

    Aug. 2020, KDD'20, [PDF]

16. Secure Efficient Federated KNN for Recommendation Systems

    Aug. 2020, ICNC-FSKD‘20, [PDF]

17. FedRec: Federated Recommendation with Explicit Feedback

    Aug. 2020, IEEE Intelligent Systems 2020, [PDF]

18. Secure Federated Matrix Factorization

    Aug. 2020, IEEE Intelligent Systems 2020, [PDF]

19. A Federated Recommender System for Online Services

    Sep. 2020, RecSys‘20, [PDF]

20. Privacy-Preserving News Recommendation Model Learning

    Oct. 2020, EMNLP'20, [PDF]

21. Federated Multi-Armed Bandits

    Dec. 2020, AAAI'21, [PDF]

22. FedRec++: Lossless Federated Recommendation with Explicit Feedback

    Dec. 2020, AAAI'21, [PDF]

23. FedeRank: User Controlled Feedback with Federated Recommender Systems

    Jan. 2021, ECIR'21, [PDF]

24. Differentially private locality sensitive hashing based federated recommender system

    Feb. 2021, Concurrency and Computation: Practice and Experience, [PDF]

25. Personalized Recommendation Algorithm for Mobile Based on Federated Matrix Factorization

    Mar. 2021, CDMMS'20, [PDF]

26. A Federated Learning Approach for Privacy Protection in Context-Aware Recommender Systems

    Apr. 2021, The Computer Journal, [PDF]

27. DeepRec: On-device Deep Learning for Privacy-Preserving Sequential Recommendation in Mobile Commerce

    Apr. 2021, WWW'21, [PDF]

28. Federated Multi-armed Bandits with Personalization

    Apr. 2021, AISTATS'21, [PDF]

29. DARES: An Asynchronous Distributed Recommender System Using Deep Reinforcement Learning

    Jun. 2021, IEEE Access, [PDF]

30. Demystifying Model Averaging for Communication-Efficient Federated Matrix Factorization

    Jun. 2021, ICASSP'21, [PDF]

31. Federated matrix factorization for privacy-preserving recommender systems

    Jul. 2021, Applied Soft Computing, [PDF]

32. Demystifying Model Averaging for Communication-Efficient Federated Matrix Factorization

    Jun. 2021, ICASSP'21, [PDF]

33. Learning Federated Representations and Recommendations with Limited Negatives

    Aug. 2021, NFFL NIPS'21, [PDF]

34. Fast-adapting and Privacy-preserving Federated Recommender System

    Sep. 2021, VLDB J 2021, [PDF]

35. A Validated Privacy-Utility Preserving Recommendation System with Local Differential Privacy

    Sep. 2021, BigDataSE 2021, [PDF]

36. Stronger Privacy for Federated Collaborative Filtering with Implicit Feedback

    Sep. 2021, RecSys‘21, [PDF]

37. Privacy Preserving Collaborative Filtering by Distributed Mediation

    Sep. 2021, RecSys‘21, [PDF]

38. Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving

    Oct. 2021, EMNLP 2021, [PDF]

ArXiv

1. Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System

   Jan. 2019, arXiv, [PDF]

2. Federating Recommendations Using Differentially Private Prototypes

   Mar. 2020, arXiv, [PDF]

3. Privacy-preserving and yet Robust Collaborative Filtering Recommender as a Service

   Oct. 2019, arXiv, [PDF]

4. FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation

   Mar. 2020, arXiv, [PDF]

5. Federated Multi-view Matrix Factorization for Personalized Recommendations

   Apr. 2020, arXiv, [PDF]

6. Survey of Privacy-Preserving Collaborative Filtering

   Apr. 2020, arXiv, [PDF]

7. Robust Federated Recommendation System

   Jun. 2020, arXiv, [PDF]

8. Shared MF: A privacy-preserving recommendation system

   Aug. 2020, ArXiv, [PDF]

9. A Novel Privacy-Preserved Recommender System Framework based on Federated Learning

   Nov. 2020, arXiv, [PDF]

10. Federated Neural Collaborative Filtering

    Jun. 2021, arXiv, [PDF]

11. Practical and Secure Federated Recommendation with Personalized Masks

    Aug. 2021, arXiv, [PDF]

12. PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion

    Oct. 2021, arXiv, [PDF]

Other Resources

• Book

  FLPI'20 - Federated Recommendation Systems [Link]

• Link:

  Paper List - AustinNeverPee/FedRecPapers [Link]

  Paper List - JimLiu96/FederatedRS [Link]