Privacy-Preserving Collaborative Filtering Using Randomized Response

Abstract

This paper proposes a new privacy-preserving recommendation method classified into a randomized perturbation scheme in which a user adds random noise to the original rating value and a server provides a disguised data to allow users to predict rating value for unseen items. The proposed scheme performs perturbation in randomized response scheme, which preserves higher degree of privacy than that of additive perturbation. To address the accuracy reduction of the randomized response, the proposed scheme uses a posterior probability distribution function, derived from Bayes' estimation to reconstruction of the original distribution, to revise the similarity between items computed from the disguised matrix. A simple experiment shows the accuracy improvement of the proposed scheme.