Local Differentially Private Matrix Factorization For Recommendations

Neera, Jeyamohan, Chen, Xiaomin and Aslam, Nauman (2019) Local Differentially Private Matrix Factorization For Recommendations. In: 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2019): 26-28 August 2019, Island of Ulkulhas, Maldives. IEEE, Piscataway, NJ, pp. 81-86. ISBN 9781728127422, 9781728127415, 9781728127408

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Abstract

In recent years recommendation systems have become popular in the e-commerce industry as they can be used to provide a personalized experience to users. However, performing analytics on users' information has also raised privacy concerns. Various privacy protection mechanisms have been proposed for recommendation systems against user-side adversaries. However most of them disregards the privacy violations caused by the service providers. In this paper, we propose a local differential privacy mechanism for matrix factorization based recommendation systems. In our mechanism, users perturb their ratings locally on their devices using Laplace and randomized response mechanisms and send the perturbed ratings to the service provider. We evaluate the proposed mechanism using Movielens dataset and demonstrate that it can achieve a satisfactory tradeoff between data utility and user privacy.

Item Type:	Book Section
Uncontrolled Keywords:	Local Differential Privacy, Matrix Factorization, Recommendation System, Laplace Mechanism, Randomized Response
Subjects:	G400 Computer Science G500 Information Systems
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Paul Burns
Date Deposited:	16 Sep 2019 17:02
Last Modified:	29 May 2020 11:05
URI:	http://nrl.northumbria.ac.uk/id/eprint/40705

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