Towards Publishing Recommendation Data With Predictive Anonymization
Recommender systems are used to predict user preferences for products or services. In order to seek better prediction techniques, data owners of recommender systems such as Netflix sometimes make their customers reviews available to the public, which raises serious privacy concerns. With only a small amount of knowledge about individuals and their ratings to some items in a recommender system, an adver sary may easily identify the users and breach their privacy. Unfortunately, most of the existing privacy models (e.g., k anonymity) cannot be directly applied to recommender sys tems. In this paper, we study the problem of privacypreserving publishing of recommendation datasets. We represent rec ommendation data as a bipartite graph, and identify several attacks that can re-identify users and determine their item ratings. To deal with these attacks, we first give formal privacy definitions for recommendation data, and then de velop a robust and efficient anonymization algorithm, Pre dictive Anonymization, to achieve our privacy goals. Our experimental results show that Predictive Anonymization can prevent the attacks with very little impact to prediction accuracy.
Anonymization Sparsity Prediction Clustering Privacy
Chih-Cheng Chang Hui (Wendy) Wang Brian Thompson Danfeng Yao
Rutgers University Department of Computer Science Piscataway, NJ, USA Stevens Institute of Technology Department of Computer Science Hoboken, NJ, USA
国际会议
北京
英文
24-35
2010-04-13（万方平台首次上网日期，不代表论文的发表时间）