A distributed anonymization scheme for privacy-preserving recommendation systems

Recommendation systems need to collect user personal data for predicting user preferences. The privacy of user personal data becomes a main concern when users are served by the recommendation system. In this paper, we study the problem of privacy-preserving recommendation in the case that the user data are locally stored in a distributed manner. We present a distributed anonymization scheme based on the proposed anonymization map. The proposed scheme allows users individually anonymize their own data without accessing each other´s data. The experiment results show that the proposed scheme can preserve the privacy of collaborative users and outperform the perturbation-based scheme.