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This paper proposes a new recommendation system preserving both privacy andutility. It relies on the local differential privacy (LDP) for the browsinguser to transmit his noisy preference profile, as perturbed Bloom filters, tothe service provider. The originality of the approach is multifold. First, asfar as we know, the approach is the first one including at the user side twoperturbation rounds - PRR (Permanent Randomized Response) and IRR(Instantaneous Randomized Response) - over a complete user profile. Second, afull validation experimentation chain is set up, with a machine learningdecoding algorithm based on neural network or XGBoost for decoding theperturbed Bloom filters and the clustering Kmeans tool for clustering users.Third, extensive experiments show that our method achieves good utility-privacytrade-off, i.e. a 90$\%$ clustering success rate, resp. 80.3$\%$ for a value ofLDP $\epsilon = 0.8$, resp. $\epsilon = 2$. Fourth, an experimental andtheoretical analysis gives concrete results on the resistance of our approachto the plausible deniability and resistance against averaging attacks.

A Validated Privacy-Utility Preserving Recommendation System with Local Differential Privacy