Generating private recommendations in a social trust network

Abstract

Recommender systems have become increasingly important in e-commerce as they can guide customers with finding personalized services and products. A variant of recommender systems that generates recommendations from a set of trusted people is recently getting more attention in social networks. However, people are concerned about their privacy as the information revealed in recommender systems, particularly in social networks, can be misused easily. A way to eliminate the privacy risks is to make the privacy-sensitive data inaccessible by means of encryption. While the private data is inaccessible to any outsiders and the server, the same functionality of the system can be achieved by processing the encrypted data. Unfortunately, the efficiency of processing encrypted data constitutes a big challenge. In this paper, we present a privacy-enhanced recommender system in a social trust network, which is designed to be highly efficient. The cryptographic protocol for generating recommendations is based on homomorphic encryption and secure multi-party computation techniques. The additional overhead with regard to computation and communication is minimized by packing data. The experimental results show that our proposal is promising to be deployed in real world.