Privacy-enhanced collaborative filtering

Shlomo Berkovsky, Yaniv Eytani, Tsvi Kuflik, Francesco Ricci

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


Current implementations of the Collaborative Filtering (CF) algo-rithm are mostly centralized and the information about users (their profiles) is stored in a single server. Centralized storage poses a severe privacy hazard, since user profiles are fully under the control of the recommendation service providers. These profiles are available to other users upon request and are trans-ferred over the network. Recent works proposed to improve the scalability of CF by distributing the stored profiles between several repositories. In this work we investigate how a decentralized approach to users' profiles storage could mitigate some of the privacy concerns of CF. The privacy hazards are resolved by storing the users' profiles only on the client-side so they are used for compu-tation similarity only on the client-side. Only a value indicating the similarity is transferred over the network, without revealing the profile itself. To further avoid the disclosure of the user's profile through a series of attacks, we propose that the users hide or obfuscate parts of their profile. Experimental results show that relatively large parts of the user's profile could be obfuscated without ham-pering the accuracy of the CF.
Original languageEnglish
Title of host publicationProceedings of the UM 2005 Workshop on Privacy-Enhanced Personalization
Place of PublicationEdinburgh, Scotland
StatePublished - 1 Jan 2005


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