Local Differentially Private Matrix Factorization with MoG for Recommendations

Neera, Jeyamohan, Chen, Xiaomin, Aslam, Nauman and Shu, Zhan (2020) Local Differentially Private Matrix Factorization with MoG for Recommendations. In: Data and Applications Security and Privacy XXXIV. Lecture Notes in Computer Science, 12122 . Springer, Cham, pp. 208-220. ISBN 9783030496685, 9783030496692

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Official URL: https://doi.org/10.1007/978-3-030-49669-2_12

Abstract

Unethical data aggregation practices of many recommendation systems have raised privacy concerns among users. Local differential privacy (LDP) based recommendation systems address this problem by perturbing a user’s original data locally in their device before sending it to the data aggregator (DA). The DA performs recommendations over perturbed data which causes substantial prediction error. To tackle privacy and utility issues with untrustworthy DA in recommendation systems, we propose a novel LDP matrix factorization (MF) with mixture of Gaussian (MoG). We use a Bounded Laplace mechanism (BLP) to perturb user’s original ratings locally. BLP restricts the perturbed ratings to a predefined output domain, thus reducing the level of noise aggregated at DA. The MoG method estimates the noise added to the original ratings, which further improves the prediction accuracy without violating the principles of differential privacy (DP). With Movielens and Jester datasets, we demonstrate that our method offers a higher prediction accuracy under strong privacy protection compared to existing LDP recommendation methods.

Item Type: Book Section
Uncontrolled Keywords: Local differential privacy, Matrix factorization, Bounded Laplace mechanism, Mixture of Gaussian
Subjects: G500 Information Systems
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 05 Mar 2021 10:05
Last Modified: 31 May 2021 14:41
URI: http://nrl.northumbria.ac.uk/id/eprint/45624

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