Local Differentially Private Matrix Factorization For Recommendations

Neera, Jeyamohan, Chen, Xiaomin and Aslam, Nauman (2019) Local Differentially Private Matrix Factorization For Recommendations. In: AI Maldives 2019 - International Workshop on Applied Artificial Intelligence, 26th - 28th August 2019, Ukulhas Island, Maldives.

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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’ private information has also raised privacy concerns. Therefore, various privacy protection mechanisms have been proposed for recommendation systems. Yet most of these methods provide privacy protection against user-side adversaries and 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 the proposed method, 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: Conference or Workshop Item (Paper)
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
Related URLs:
Depositing User: Paul Burns
Date Deposited: 16 Sep 2019 17:02
Last Modified: 10 Oct 2019 15:16
URI: http://nrl.northumbria.ac.uk/id/eprint/40705

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