A practical privacy-preserving recommender system

Recommender systems [1] provide meaningful and useful recommendations to users by making use of explicit and implicit information about user preferences. Recommendations are also often based on the degree of similarity between the active user and all other users, or one particular item that the user has rated and all other items. The items can be of any type: books, movies, web pages, restaurants, sightseeing places, online news, and even lifestyles. By collecting information about users' preferences for different items, a recommender system creates their profiles. These preferences can help the recommender system to predict other items that might also be of interest to the user in the future. Content-based filtering (CBF) and collaborative filtering (CF) are the most commonly used techniques that generate recommendations for users based on their preferences. CBF predicts a user's rating on a particular item based on the previous ratings and item features, while CF generates recommendations based on the previous ratings only. In order to run the process of recommendations, users' profiles must be available to the recommender server (or service providers). Therefore, there are risks that such information is leaked to malicious parties which can lead to severe damage to the user's privacy (e.g. exposure or generating false recommendations) [2]. Figure 1 shows the general architecture of a conventional recommender system and possible ways in which privacy breaches can occur. It is thus crucial to adequately protect privacy of information managed by recommender systems. Existing approaches can be categorized as follows.