A Method for Privacy-preserving Collaborative Filtering Recommendations

With the continuous growth of the Internet and the progress of electronic commerce the issues of product recommendation and privacy protection are becoming increasingly important. Recommender Systems aim to solve the information overload problem by providing accurate recommendations of items to users. Collaborative filtering is considered the most widely used recommendation method for providing recommendations of items or users to other users in online environments. Additionally, collaborative filtering methods can be used with a trust network, thus delivering to the user recommendations from both a database of ratings and from users who the person who made the request knows and trusts. On the other hand, the users are having privacy concerns and are not willing to submit the required information (e.g., ratings for products), thus making the recommender system unusable. In this paper, we propose (a) an approach to product recommendation that is based on collaborative filtering and uses a combination of a ratings network with a trust network of the user to provide recommendations and (b) "neighbourhood privacy" that employs a modified privacy-aware role-based access control model that can be applied to databases that utilize recommender systems. Our proposed approach (1) protects user privacy with a small decrease in the accuracy of the recommendations and (2) uses information from the trust network to increase the accuracy of the recommendations, while, (3) providing privacy-preserving recommendations, as accurate as the recommendations provided without the privacy-preserving approach or the method that increased the accuracy applied.