Optimal Forgery and Suppression of Ratings for Privacy Enhancement in Recommendation Systems

TL;DR

This paper presents a theoretical framework for optimizing the balance between user privacy and data utility in recommendation systems by using forgery and suppression of ratings, providing a closed-form solution for the optimal trade-off.

Contribution

It introduces a quantitative analysis of privacy-utility trade-off using information-theoretic measures and derives a closed-form solution for optimal rating perturbation strategies.

Findings

Optimal forgery and suppression rates maximize privacy while maintaining acceptable utility.

Theoretical characterization of the privacy-utility trade-off.

Experimental validation on a popular recommendation system dataset.

Abstract

Recommendation systems are information-filtering systems that tailor information to users on the basis of knowledge about their preferences. The ability of these systems to profile users is what enables such intelligent functionality, but at the same time, it is the source of serious privacy concerns. In this paper we investigate a privacy-enhancing technology that aims at hindering an attacker in its efforts to accurately profile users based on the items they rate. Our approach capitalizes on the combination of two perturbative mechanisms---the forgery and the suppression of ratings. While this technique enhances user privacy to a certain extent, it inevitably comes at the cost of a loss in data utility, namely a degradation of the recommendation's accuracy. In short, it poses a trade-off between privacy and utility. The theoretical analysis of said trade-off is the object of this work. We measure privacy as the Kullback-Leibler divergence between the user's and the population's item distributions, and quantify utility as the proportion of ratings users consent to forge and eliminate. Equipped with these quantitative measures, we find a closed-form solution to the problem of optimal forgery and suppression of ratings, and characterize the trade-off among privacy, forgery rate and suppression rate. Experimental results on a popular recommendation system show how our approach may contribute to privacy enhancement.