Privacy-preserving social recommendations in geosocial networks

Geosocial networks like Foursquare have enabled people to conveniently share their whereabouts with their friends online, such as sharing check-ins at visited venues. This information could be utilized by recommender systems to improve the recommendation accuracy, known as social recommendations. However, incorporating social context into recommender systems introduces new privacy threats to users. We design a framework to achieve the benefits of social recommendations while preserving the privacy of social relations and considering the business interests of the service provider (SP). Namely, we propose that each user manages social relations locally and participates in computing social recommendations without revealing social relations to the SP and without the SP revealing proprietary information to a user. In addition, we identify three classes of inference attacks where the SP may infer the existence of social relations by monitoring users' individual check-in histories. Furthermore, we propose using private check-ins to defend against such attacks. Finally, we conduct a comprehensive performance evaluation over large-scale real-world datasets. The results suggest that the proposed privacy-preserving framework is feasible on a smart phone and only slightly affects the overall performance of recommender systems.