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Privacy preserving collaborative filtering via the Johnson-Lindenstrauss transform

conference contribution
posted on 2017-01-01, 00:00 authored by Mengmeng Yang, Tianqing Zhu, L Ma, Yang Xiang, Wanlei Zhou
Recommendation systems have become increasingly popular as a result of the significant growth in online information. However, it requires a substantial amount of historical user data to generate accurate predictions, which raises concerns over user privacy. Differential privacy is a well acknowledged privacy notion that has become an important standard for the preservation of privacy. Unfortunately, existing privacy preservation methods based on differential privacy protect user privacy at the cost of utility, aspects of which have to be sacrificed to ensure that privacy is maintained. In this paper, we propose a Johnson Lindenstrauss privacy preserving collaborative filtering (JLCF) method. The proposed method preserves users' privacy without compromising utility. It guarantees user privacy by directly perturbing the original dataset using a transfer matrix. We prove that the proposed method achieves-differential privacy. In addition, we theoretically analyse the utility of the proposed method, and our extensive experiments show that the prediction accuracy is improved.

History

Event

IEEE TrustCom / BigDataSE / ICESS. Conference (16th : 2017 : Sydney, N.S.W.)

Pagination

417 - 424

Publisher

IEEE

Location

Sydney, Australia

Place of publication

Piscataway, N.J.

Start date

2017-08-01

End date

2017-08-04

ISSN

2324-9013

ISBN-13

9781509049066

Language

eng

Notes

IEEE TrustCom / BigDataSE / ICESS 2017 comprises 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering, and 14th IEEE International Conference On Embedded Software And Systems

Publication classification

E1 Full written paper - refereed

Copyright notice

2017, IEEE

Editor/Contributor(s)

Unknown

Title of proceedings

TrustCom / BigDataSE / ICESS 2017 : proceedings of the TrustCom / BigDataSE / ICESS 2017 international conference

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