Privacy-Preserving Distributed Collaborative Filtering

We propose a new mechanism to preserve privacy while leveraging user profiles in distributed recommender systems. Our mechanism relies on (i) an original obfuscation scheme to hide the exact profiles of users without significantly decreasing their utility, as well as on (ii) a randomized dissemination protocol ensuring differential privacy during the dissemination process. We compare our mechanism with a non-private as well as with a fully private alternative. We consider a real dataset from a user survey and report on simulations as well as planetlab experiments. We dissect our results in terms of accuracy and privacy trade-offs, bandwidth consumption, as well as resilience to a censorship attack. In short, our extensive evaluation shows that our twofold mechanism provides a good trade-off between privacy and accuracy, with little overhead and high resilience.