A privacy-preserving framework for cross-domain recommender systems

Abstract

User privacy in the recommender systems have received much attention over the years. However, much of this attention has been on privacy protection in single-domain recommender systems and not on cross-domain recommender systems. The privacy-preserving cross-domain recommender systems not only encourages collaboration of data between different domains to solve the problem of data sparsity but also ensures the users’ privacy and secure transfer of auxiliary information between domains. However, existing studies are not suitable for privacy protection in a cross-domain scenario. To this end, we propose a novel privacy-preserving framework for cross-domain recommender systems that provides a generic template for other secure cross-domain recommender systems. Employing a homomorphic encryption scheme, the framework consists of two protocols for users’ privacy in cross-domain recommender systems. We mathematically described every step involved in each protocol, proved that the two protocols are secure against a semi-honest adversary, and compared the complexity of the protocols.