research-article

Efficient Federated Matrix Factorization Against Inference Attacks

Authors:

Qiang YangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 13, Issue 4

Article No.: 59, Pages 1 - 20

https://doi.org/10.1145/3501812

Published: 28 June 2022 Publication History

Abstract

Recommender systems typically require the revelation of users’ ratings to the recommender server, which will subsequently use these ratings to provide personalized services. However, such revelations make users vulnerable to a broader set of inference attacks, allowing the recommender server to learn users’ private attributes, e.g., age and gender. Therefore, in this paper, we propose an efficient federated matrix factorization method that protects users against inference attacks. The key idea is that we obfuscate one user’s rating to another such that the private attribute leakage is minimized under the given distortion budget, which bounds the recommending loss and overhead of system efficiency. During the obfuscation, we apply differential privacy to control the information leakage between the users. We also adopt homomorphic encryption to protect the intermediate results during training. Our framework is implemented and tested on real-world datasets. The result shows that our method can reduce up to 16.7% of inference attack accuracy compared to using no privacy protections.

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Cited By

Wang LZhou HBao YYan XShen GKong X(2024)Horizontal Federated Recommender System: A SurveyACM Computing Surveys10.1145/365616556:9(1-42)Online publication date: 8-May-2024
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Efficient Federated Matrix Factorization Against Inference Attacks

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 13, Issue 4

August 2022

364 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3522732

Editor:
Huan Liu
Arizona State University, USA

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Publication History

Published: 28 June 2022

Online AM: 17 May 2022

Accepted: 01 November 2021

Revised: 01 August 2021

Received: 01 March 2021

Published in TIST Volume 13, Issue 4

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Cited By

Wang LZhou HBao YYan XShen GKong X(2024)Horizontal Federated Recommender System: A SurveyACM Computing Surveys10.1145/365616556:9(1-42)Online publication date: 8-May-2024
https://dl.acm.org/doi/10.1145/3656165
DeSmet CCook D(2024)HydraGAN: A Cooperative Agent Model for Multi-Objective Data GenerationACM Transactions on Intelligent Systems and Technology10.1145/365398215:3(1-21)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1145/3653982
Yin HChen TQu LCui BChua TNgo CKumar RLauw HKa-Wei Lee R(2024)On-Device Recommender Systems: A Tutorial on The New-Generation Recommendation ParadigmCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3641250(1280-1283)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3641250
Zheng XGuan MJia XSun LLuo Y(2024)Federated Matrix Factorization Recommendation Based on Secret Sharing for Privacy PreservingIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.332282411:3(3525-3535)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3322824
Xie QJiang SJiang LHuang YZhao ZKhan SDai WLiu ZWu K(2024)Efficiency Optimization Techniques in Privacy-Preserving Federated Learning With Homomorphic Encryption: A Brief SurveyIEEE Internet of Things Journal10.1109/JIOT.2024.338287511:14(24569-24580)Online publication date: 15-Jul-2024
https://doi.org/10.1109/JIOT.2024.3382875
Wang LKong XLi JShu CShen G(2024)Exploring Personalized Intents with Knowledge Graph for Federated Self-supervised RecommendationAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5663-6_34(402-414)Online publication date: 5-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-5663-6_34
Müllner PLex ESchedl MKowald D(2023)Differential privacy in collaborative filtering recommender systems: a reviewFrontiers in Big Data10.3389/fdata.2023.12499976Online publication date: 12-Oct-2023
https://doi.org/10.3389/fdata.2023.1249997
Müllner PLex ESchedl MKowald D(2023)ReuseKNN: Neighborhood Reuse for Differentially Private KNN-Based RecommendationsACM Transactions on Intelligent Systems and Technology10.1145/360848114:5(1-29)Online publication date: 11-Aug-2023
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Ren ZCheng XFan MZhang JHong C(2023)Communication Efficient Secret Sharing with Dynamic Communication-Computation ConversionIEEE INFOCOM 2023 - IEEE Conference on Computer Communications10.1109/INFOCOM53939.2023.10228872(1-10)Online publication date: 17-May-2023
https://doi.org/10.1109/INFOCOM53939.2023.10228872
Ding XLi GYuan LZhang LRong Q(2023)Efficient federated item similarity model for privacy-preserving recommendationInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10347060:5Online publication date: 1-Sep-2023
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