2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) (2017)
Denver, CO, United States
June 26, 2017 to June 29, 2017
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/DSN.2017.22
Recommenders widely use collaborative filtering schemes. These schemes, however, threaten privacy as user profiles are made available to the service provider hosting the recommender and can even be guessed by curious users who analyze the recommendations. Users can encrypt their profiles to hide them from the service provider and add noise to make them difficult to guess. These precautionary measures hamper latency and recommendation quality. In this paper, we present a novel recommender, X-REC, enabling an effective collaborative filtering scheme to ensure the privacy of users against the service provider (system-level privacy) or other users (user-level privacy). X-REC builds on two underlying services: X-HE, an encryption scheme designed for recommenders, and X-NN, a neighborhood selection protocol over encrypted profiles. We leverage uniform sampling to ensure differential privacy against curious users. Our extensive evaluation demonstrates that X-REC provides (1) recommendation quality similar to non-private recommenders, and (2) significant latency improvement over privacy-aware alternatives.
Privacy, Servers, Encryption, Data privacy, Computer architecture, Prediction algorithms
R. Guerraoui, A. Kermarrec, R. Patra, M. Valiyev and J. Wang, "I Know Nothing about You But Here is What You Might Like," 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), Denver, CO, United States, 2017, pp. 439-450.