标题：Differential Privacy for Collaborative Filtering Recommender Algorithm
作者：Zhu, Xue; Sun, Yuqing
作者机构：[Zhu, Xue] Shandong Univ, Dept Software Engn, Jinan, Shandong, Peoples R China.; [Zhu, Xue] Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peo 更多
会议名称：ACM International Workshop on Security and Privacy Analytics (IWSPA)
会议日期：MAR 11, 2016
来源：IWSPA'16: PROCEEDINGS OF THE 2016 ACM INTERNATIONAL WORKSHOP ON SECURITY AND PRIVACY ANALYTICS
关键词：Recommendation; Collaborative Filtering; Inference Attack; Differential; Privacy
摘要：Collaborative filtering plays an essential role in a recommender system, which recommends a list of items to a user by learning behavior patterns from user rating matrix. However, if an attacker has some auxiliary knowledge about a user purchase history, he/she can infer more information about this user. This brings great threats to user privacy. Some methods adopt differential privacy algorithms in collaborative filtering by adding noises to a rating matrix. Although they provide theoretically private results, the influence on recommendation accuracy are not discussed. In this paper, we solve the privacy problem in recommender system in a different way by applying the differential privacy method into the procedure of recommendation. We design two differentially private recommender algorithms with sampling, named Differentially Private Item Based Recommendation with sampling (DP-IR for short) and Differentially Private User Based Recommendation with sampling(DP-UR for short). Both algorithms are based on the exponential mechanism with a carefully designed quality function. Theoretical analyses on privacy of these algorithms are presented. We also investigate the accuracy of the proposed method and give theoretical results. Experiments are performed on real datasets to verify our methods.