research-article

Applying Differential Privacy to Matrix Factorization

Authors:

Arnaud Berlioz,

Mohamed Ali Kaafar,

Roksana Boreli,

Shlomo BerkovskyAuthors Info & Claims

RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

Pages 107 - 114

https://doi.org/10.1145/2792838.2800173

Published: 16 September 2015 Publication History

Abstract

Recommender systems are increasingly becoming an integral part of on-line services. As the recommendations rely on personal user information, there is an inherent loss of privacy resulting from the use of such systems. While several works studied privacy-enhanced neighborhood-based recommendations, little attention has been paid to privacy preserving latent factor models, like those represented by matrix factorization techniques. In this paper, we address the problem of privacy preserving matrix factorization by utilizing differential privacy, a rigorous and provable privacy preserving method. We propose and study several approaches for applying differential privacy to matrix factorization, and evaluate the privacy-accuracy trade-offs offered by each approach. We show that input perturbation yields the best recommendation accuracy, while guaranteeing a solid level of privacy protection.

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Cited By

Dai HZhu MGui X(2024)LSH Models in Federated RecommendationApplied Sciences10.3390/app1411442314:11(4423)Online publication date: 23-May-2024
https://doi.org/10.3390/app14114423
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
https://doi.org/10.1145/3652891
Hu WFang H(2024)Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network ApproachACM Transactions on Knowledge Discovery from Data10.1145/3643821Online publication date: 30-Jan-2024
https://doi.org/10.1145/3643821
Show More Cited By

Index Terms

Applying Differential Privacy to Matrix Factorization
1. Information systems
  1. World Wide Web
    1. Web applications
    2. Web services

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Highlights
- An effective privacy-preserving recommendation method is proposed.
- Our method ...
New SVD-based collaborative filtering algorithms with differential privacy

In the era of big data, real and reliable user data information is an important factor in the recommendation technology; therefore, the disclosure of personal privacy has become a significant problem user concern. Differential privacy protection is a ...

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Published In

cover image ACM Conferences

RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

September 2015

414 pages

ISBN:9781450336925

DOI:10.1145/2792838

General Chairs:
Hannes Werthner
TU Wien, Austria
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria
,
Program Chairs:
Jennifer Golbeck
University of Maryland, USA
,
Giovanni Semeraro
University of Bari Aldo Moro, Italy

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 September 2015

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RecSys '15

Sponsor:

SIGCHI

RecSys '15: Ninth ACM Conference on Recommender Systems

September 16 - 20, 2015

Vienna, Austria

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RecSys '15 Paper Acceptance Rate 28 of 131 submissions, 21%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Dai HZhu MGui X(2024)LSH Models in Federated RecommendationApplied Sciences10.3390/app1411442314:11(4423)Online publication date: 23-May-2024
https://doi.org/10.3390/app14114423
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
https://doi.org/10.1145/3652891
Hu WFang H(2024)Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network ApproachACM Transactions on Knowledge Discovery from Data10.1145/3643821Online publication date: 30-Jan-2024
https://doi.org/10.1145/3643821
Saha SImtiaz H(2024)Privacy-Preserving Non-Negative Matrix Factorization with OutliersACM Transactions on Knowledge Discovery from Data10.1145/363296118:3(1-26)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3632961
Zhang SYuan WYin H(2024)Comprehensive Privacy Analysis on Federated Recommender System Against Attribute Inference AttacksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.329560136:3(987-999)Online publication date: Mar-2024
https://doi.org/10.1109/TKDE.2023.3295601
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
Cai ZTang TYu SXiao YXia F(2024)Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender SystemsIEEE Internet of Things Journal10.1109/JIOT.2023.332936311:6(10384-10397)Online publication date: 15-Mar-2024
https://doi.org/10.1109/JIOT.2023.3329363
Mugdho SImtiaz H(2024)Privacy-preserving matrix factorization for recommendation systems using Gaussian mechanism and functional mechanismInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02276-315:12(5745-5763)Online publication date: 14-Jul-2024
https://doi.org/10.1007/s13042-024-02276-3
Lin AWang JZhu ZCaverlee J(2024)Federated Conversational Recommender SystemsAdvances in Information Retrieval10.1007/978-3-031-56069-9_4(50-65)Online publication date: 23-Mar-2024
https://doi.org/10.1007/978-3-031-56069-9_4
Bi ZDuan YZhang WShan M(2024)A Multi-behavior Recommendation Algorithm Based on Personalized Federated LearningCollaborative Computing: Networking, Applications and Worksharing10.1007/978-3-031-54531-3_8(134-153)Online publication date: 23-Feb-2024
https://doi.org/10.1007/978-3-031-54531-3_8
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