A secure and efficient framework for privacy preserving social recommendation

The well-known cold start problem in traditional collaborative filtering based recommender systems can be effectively addressed by social recommendation, which has been witnessed by a number of researches recently in many application domains. The social graph used in social recommendation is typically owned by a third party such as Facebook and Twitter, and should be hidden from recommender systems for obvious reasons of commercial benefits, as well as due to privacy legislation. In this paper, we present a secure and efficient framework for privacy preserving social recommendation. Our framework is built on mature cryptographic building blocks, including Paillier cryptosystem and Yao’ protocol, which lays a solid foundation for the security of our framework. Using our framework, the owner of sales data and the owner of social graph can cooperatively compute social recommendation, without revealing their private data to each other. We theoretically prove the security and analyze the complexity of our framework. Empirical study shows our framework has a linear complexity with respect to the number of users and items in recommender systems and is practical in real applications.