Enhancing privacy and preserving accuracy of a distributed collaborative filtering

Shlomo Berkovsky, Yaniv Eytani, Tsvi Kuflik, Francesco Ricci

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

99 Citations (Scopus)


Collaborative Filtering (CF) is a powerful technique for generating personalized predictions. CF systems are typically based on a central storage of user profiles used for generating the recommendations. However, such centralized storage introduces a severe privacy breach, since the profiles may be accessed for purposes, possibly malicious, not related to the recommendation process. Recent researches proposed to protect the privacy of CF by distributing the profiles between multiple repositories and exchange only a subset of the profile data, which is useful for the recommendation. This work investigates how a decentralized distributed storage of user profiles combined with data modification techniques may mitigate some privacy issues. Results of experimental evaluation show that parts of the user profiles can be modified without hampering the accuracy of CF predictions. The experiments also indicate which parts of the user profiles are most useful for generating accurate CF predictions, while their exposure still keeps the essential privacy of the users.
Original languageEnglish
Title of host publicationProceedings of the 2007 ACM conference on Recommender systems, RecSys '07
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Electronic)9781595937308
Publication statusPublished - 2007
Externally publishedYes
EventACM Conference on Recommender Systems, RecSys 2007 - Minneapolis, United States
Duration: 19 Oct 200720 Oct 2007


ConferenceACM Conference on Recommender Systems, RecSys 2007
Country/TerritoryUnited States


  • Collaborative Filtering
  • Recommender Systems
  • Privacy


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