Differential Privacy Protection for Collaborative Filtering Algorithms with Explicit and Implicit Trust

被引：0

作者：

Xian Z.-Z. ^{[1
,2
]}

Li Q.-L. ^{[2
]}

Huang X.-Y. ^{[3
]}

Lu J.-Y. ^{[1
]}

Li L. ^{[2
]}

机构：

[1] School of Internet Finance and Information Engineering, Guangdong University of Finance, Guangzhou, 510521, Guangdong

[2] School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510006, Guangdong

[3] School of Economics and Commerce, South China University of Technology, Guangzhou, 510006, Guangdong

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2018年 / 46卷 / 12期

关键词：

Differential privacy; Implicit trust; Matrix factorization; Personal privacy preservation; Social collaborative filtering; Trust relationship;

D O I：

10.3969/j.issn.0372-2112.2018.12.032

中图分类号：

学科分类号：

摘要：

TrustSVD, a popular social collaborative filtering algorithm that incorporates both of the explicit and implicit trust information, has been widely used in recommender systems.However, there is a risk of disclosure of user privacy in TrustSVD.Privacy information inference based on background knowledge is one of the great hidden dangers of user's privacy disclosure.Differential privacy has attracted much attentiaon as a privacy protection mechanism that can provide a strict theoretical guarantee for protection objects.In this article, we propose DPTrustSVD, a novel collaborative filtering algorithm that applies Differential privacy to TrustSVD and has the ability of privacy preserving.Theoretical analysis and experimental results show that DPTrustSVD not only provides a strict theoretical guarantee for users' privacy information, but also maintains a high prediction accuracy. © 2018, Chinese Institute of Electronics. All right reserved.

引用

页码：3050 / 3059

页数：9

共 25 条

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