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 contributionpeer-review

90 Citations (Scopus)


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
Number of pages8
ISBN (Electronic)9781450336925
Publication statusPublished - 16 Sep 2015
Externally publishedYes
Event9th ACM Conference on Recommender Systems, RecSys 2015 - Vienna, Austria
Duration: 16 Sep 201520 Sep 2015


Conference9th ACM Conference on Recommender Systems, RecSys 2015


  • differential privacy
  • matrix factorization


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