Matrix Factorization Recommendation Algorithm for Differential Privacy Protection

被引：0

作者：

Wang Y. ^{[1
,2
]}

Ran X. ^{[1
]}

Yin E.-M. ^{[1
]}

Wang L. ^{[1
]}

机构：

[1] Key Laboratory of E-Commerce and Modern Logistics, Chongqing University of Posts and Telecommunications, Chongqing

[2] Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology, Guilin

来源：

Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2021年 / 50卷 / 03期

关键词：

Collaborative filtering; Differential privacy; Genetic algorithm; Matrix factorization;

D O I：

10.12178/1001-0548.2020359

中图分类号：

学科分类号：

摘要：

Collaborative filtering techniques require tremendous amount of personal data to provide personalized recommendation services, which has caused the rising concerns about the risk of privacy leakage. Most existed methods for implementing privacy protection in recommender systems are prone to introduce excessive noises, which significantly degrades the recommendation quality. To address this problem, a matrix factorization algorithm satisfying differential privacy is proposed. The method first converts the matrix factorization problem into two alternate optimization problems, in which user latent factors and item latent factors are optimized respectively. Then a genetic algorithm is introduced to solve these two optimization problems, in which the enhanced exponential mechanism is applied into the individual selection and a novel mutation operation is designed based on the idea of finding important latent factors. Theoretical analysis and experimental results show that the algorithm can not only provide strong differential privacy protection for user data, but also ensure the accuracy of recommendations. Therefore, it has good application value in recommender systems. Copyright ©2020 Journal of University of Electronic Science and Technology of China. All rights reserved.

引用

页码：405 / 413

页数：8

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