A privacy-preserving mobile application recommender system based on trust evaluation

Abstract

Too many mobile applications in App stores results in information overload in App market. Mobile users are confused in choosing suitable and trustworthy mobile applications due to a large number of available candidates. A mobile application recommender system is a powerful tool that helps users solve this problem. However, there are few feasible recommender systems focusing on recommending mobile applications in the literature. First, few researches study user trust behavior based recommendation on mobile applications. Second, the accuracy and personalization of existing recommender systems need to be further improved. Particularly, privacy preservation is still an open issue in mobile application recommendation. In this paper, we propose two privacy-preserving mobile application recommendation schemes based on trust evaluation. Recommendations on mobile application are generated based on user trust behaviors of mobile application usage. In these two schemes, user private data can be preserved by applying our proposed security protocols and utilizing homomorphic encryption. We further implement two schemes and develop two mobile Apps that can be applied in different scenarios, i.e., a centralized cloud service and distributed social networking. Security analysis, performance evaluation and simulation results show that our schemes have sound security, efficiency, accuracy, and robustness.