Controlling privacy in recommender systems
Controlling privacy in recommender systems
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发表时间：
2014-12
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通讯作者：
Yu Xin;T. Jaakkola
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作者：
Yu Xin;T. Jaakkola
推荐系统涉及到一个内在的权衡之间的准确性的建议和用户愿意释放他们的喜好信息的程度。在本文中，我们探讨了隐私的两层概念，其中有一小部分“公共”用户愿意公开分享他们的偏好，而大量“私人”用户需要隐私保证。我们从理论上和经验证明，一个适度数量的公共用户没有访问私人用户信息已经足够的合理准确性。此外，我们引入了一个新的隐私概念，收集私人用户的关系信息，同时保持一阶否认。我们展示了从控制访问私人用户偏好中获得的收益。
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.