Private and Utility Enhanced Recommendations with Local Differential Privacy and Gaussian Mixture Model

TL;DR

This paper introduces a privacy-preserving recommendation system that combines local differential privacy with a Gaussian Mixture Model to improve accuracy while maintaining user privacy.

Contribution

It proposes a novel LDP-based matrix factorization method using a Gaussian Mixture Model and a Bounded Laplace mechanism to enhance recommendation accuracy under privacy constraints.

Findings

Significant accuracy improvements on Movielens, Libimseti, and Jester datasets.

Effective noise regulation with the Bounded Laplace mechanism.

Maintains strong privacy guarantees with epsilon-LDP.

Abstract

Recommendation systems rely heavily on users behavioural and preferential data (e.g. ratings, likes) to produce accurate recommendations. However, users experience privacy concerns due to unethical data aggregation and analytical practices carried out by the Service Providers (SP). Local differential privacy (LDP) based perturbation mechanisms add noise to users data at user side before sending it to the SP. The SP then uses the perturbed data to perform recommendations. Although LDP protects the privacy of users from SP, it causes a substantial decline in predictive accuracy. To address this issue, we propose an LDP-based Matrix Factorization (MF) with a Gaussian Mixture Model (MoG). The LDP perturbation mechanism, Bounded Laplace (BLP), regulates the effect of noise by confining the perturbed ratings to a predetermined domain. We derive a sufficient condition of the scale parameter for BLP to satisfy $\epsilon$ LDP. At the SP, The MoG model estimates the noise added to perturbed ratings and the MF algorithm predicts missing ratings. Our proposed LDP based recommendation system improves the recommendation accuracy without violating LDP principles. The empirical evaluations carried out on three real world datasets, i.e., Movielens, Libimseti and Jester, demonstrate that our method offers a substantial increase in predictive accuracy under strong privacy guarantee.