NDLI: Differential Privacy for Collaborative Filtering Recommender Algorithm

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Differential Privacy for Collaborative Filtering Recommender Algorithm

Content Provider	ACM Digital Library
Author	Sun, Yuqing Zhu, Xue
Abstract	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.
Starting Page	9
Ending Page	16
Page Count	8
File Format	PDF
ISBN	9781450340779
DOI	10.1145/2875475.2875483
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-03-11
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Differential privacy Collaborative filtering Inference attack Recommendation
Content Type	Text
Resource Type	Article

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