Controlling privacy in recommender systems
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Jaakkola_Controlling privacy.pdf
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231.64 KB
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Author(s)
Xin, Yu
Jaakkola, Tommi S.
Date Issued
2014
Journal
Advances in Neural Information Processing Systems (NIPS)
Publisher
Neural Information Processing Systems
Citation
Xin, Yu, and Tommi Jaakkola. "Controlling privacy in recommender systems." Advances in Neural Information Processing Systems 27 (NIPS 2014).
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://papers.nips.cc/paper/5359-controlling-privacy-in-recommender-systems
