Privacy-Enhanced Collaborative Filtering

Current implementations of the Collaborative Filtering (CF) algorithm 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 transferred 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 computation 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 hampering the accuracy of the C