Differential privacy for neighborhood-based collaborative filtering

Authors:
Tianqing Zhu;Gang Li;Yongli Ren;Wanlei Zhou;Ping Xiong
Affiliations:
Wuhan Polytechnic University and Deakin University;Deakin University;Deakin University;Deakin University;Zhongnan University of Economics and Law, China
Venue:
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Year:
2013

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Abstract

As a popular technique in recommender systems, Collaborative Filtering (CF) has received extensive attention in recent years. However, its privacy-related issues, especially for neighborhood-based CF methods, can not be overlooked. The aim of this study is to address the privacy issues in the context of neighborhood-based CF methods by proposing a Private Neighbor Collaborative Filtering (PNCF) algorithm. The algorithm includes two privacy-preserving operations: Private Neighbor Selection and Recommendation-Aware Sensitivity. Private Neighbor Selection is constructed on the basis of the notion of differential privacy to privately choose neighbors. Recommendation-Aware Sensitivity is introduced to enhance the performance of recommendations. Theoretical and experimental analysis are provided to show the proposed algorithm can preserve differential privacy while retaining the accuracy of recommendations.