Controlling privacy in recommender systems
Author(s)Xin, Yu; Jaakkola, Tommi S.
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Recommender systems involve an inherent trade-off between accuracy of recommendations and the extent to which users are willing to release information about their preferences. In this paper, we explore a two-tiered notion of privacy where there is a small set of public'' users who are willing to share their preferences openly, and a large set of private'' users who require privacy guarantees. We show theoretically and demonstrate empirically that a moderate number of public users with no access to private user information already suffices for reasonable accuracy. Moreover, we introduce a new privacy concept for gleaning relational information from private users while maintaining a first order deniability. We demonstrate gains from controlled access to private user preferences.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Advances in Neural Information Processing Systems (NIPS)
Neural Information Processing Systems
Xin, Yu, and Tommi Jaakkola. "Controlling privacy in recommender systems." Advances in Neural Information Processing Systems 27 (NIPS 2014).
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