Applying differential privacy to matrix factorization

Arnaud Berlioz, Arik Friedman, Mohamed Ali Kaafar, Roksana Boreli, Shlomo Berkovsky

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

38 Citations (Scopus)

Abstract

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

Original languageEnglish
Title of host publicationProceedings of the 9th ACM Conference on Recommender Systems
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages107-114
Number of pages8
ISBN (Electronic)9781450336925
DOIs
Publication statusPublished - 16 Sep 2015
Externally publishedYes
Event9th ACM Conference on Recommender Systems, RecSys 2015 - Vienna, Austria
Duration: 16 Sep 201520 Sep 2015

Conference

Conference9th ACM Conference on Recommender Systems, RecSys 2015
CountryAustria
CityVienna
Period16/09/1520/09/15

Keywords

  • differential privacy
  • matrix factorization

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