SecRec: A Privacy-Preserving Method for the Context-Aware Recommendation System

Abstract

Context-aware recommendation systems are of increasing popularity in the digital era to recommend personalized items to users. However, how to ensure user data privacy while remaining high recommendation accuracy is widely considered a challenge. In this work, we propose a privacy-preserving method for the context-aware recommendation system in the two-cloud model. In particular, we first adjust the standard additive secret sharing scheme to support secure negative integers computation, based on which we manage to design secure comparison protocol and division protocols that enjoy desirable security and efficiency. By using these new protocols, we propose a secure and efficient context-aware recommendation system that also supports offline users. Compared with the state-of-the-art, our scheme achieves stronger data privacy preservation by further protecting the intermediate data calculated during the system training. Experimental results on real-world datasets indicate that our scheme is efficient. Notable, our system could achieve more significant performance improvement by running the underlying schemes in parallel.