In online systems of videos, music or books, users’ behaviors are disclosed to the recommender systems to learn their interests. Such a disclosure raises a serious concern in the public for the leak of users’ privacy. Meanwhile, some algorithms are proposed to obfuscate users’ historical behavior records to protect users’ privacy, at the cost of degradation of recommendation accuracy. It is a common belief that such tradeoff is inevitable. In this paper, however, we break this pessimistic belief based on the fact that people's interests are not necessarily limited to items which are geared to a certain gender, age, or profession. Based on this idea, we propose a recommendation-friendly privacy-preserving framework by introducing a privacy-preserving module between a recommender system and user side. For instance, to obfuscate a female user's gender information, the privacy-preserving module adds a set of extra factitious ratings of movies not watched by the given user. These added movies are selected to be those mostly watched by male viewers but interesting the given female user. Extensive experiments show that our algorithm obfuscates users’ privacy information, e.g., gender, efficiently, but also maintains or even improves recommendation accuracy. 
Can user privacy and recommendation performance be preserved simultaneously?