Privacy-conscious P2P data sharing scheme with bogus profile distribution
Web Intelligence and Agent Systems
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This paper explores a method to realize an anonymityconscious P2P data sharing network. The proposed network lets users extract other users' data that match the users' profiles, thereby providing them a collaborative filtering-based data recommendation. Our proposal realizes such a recommendation scheme without forcing the users to disclose their profiles. Our method intentionally fills the network with "white lies," whereby bogus user profiles are distributed throughout the network to protect users' anonymity without harming the overall effectiveness of the data exchange.