APPLET: a privacy-preserving framework for location-aware recommender system

Location-aware recommender systems that use location-based ratings to produce recommendations have recently experienced a rapid development and draw significant attention from the research community. However, current work mainly focused on high-quality recommendations while underestimating privacy issues, which can lead to problems of privacy. Such problems are more prominent when service providers, who have limited computational and storage resources, leverage on cloud platforms to fit in with the tremendous number of service requirements and users. In this paper, we propose a novel framework, namely APPLET, for protecting user privacy information, including locations and recommendation results, within a cloud environment. Through this framework, all historical ratings are stored and calculated in ciphertext, allowing us to securely compute the similarities of venues through Paillier encryption, and predict the recommendation results based on Paillier, commutative, and comparable encryption. We also theoretically prove that user information is private and will not be leaked during a recommendation. Finally, empirical results over a real-world dataset demonstrate that our framework can efficiently recommend POIs with a high degree of accuracy in a privacy-preserving manner.