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 two contributions: (i) an original obfuscation scheme, and (ii) a randomized dissemination protocol. We show that our obfuscation scheme hides the exact profiles of users without significantly decreasing their utility for recommendation. In addition, we precisely characterize the conditions that make our randomized dissemination protocol differentially private. 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.