Local Differentially Private Matrix Factorization For Recommendations

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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.
Original languageEnglish
Title of host publication2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2019)
Subtitle of host publication26-28 August 2019, Island of Ulkulhas, Maldives
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages81-86
ISBN (Electronic)9781728127415, 9781728127408
ISBN (Print)9781728127422
DOIs
Publication statusPublished - Aug 2019
Event13th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019 - Island of Ulkulhas, Maldives
Duration: 26 Aug 201928 Aug 2019

Conference

Conference13th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019
CountryMaldives
CityIsland of Ulkulhas
Period26/08/1928/08/19

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