Towards publishing recommendation data with predictive anonymization
Chih-Cheng Chang, Brian Thompson, Hui Wang, Danfeng Daphne Yao · 2010
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 adversary 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 systems.