Differential Privacy Protection for Collaborative Filtering Algorithms with Explicit and Implicit Trust
XIAN Zheng-zheng1,2, LI Qi-liang2, HUANG Xiao-yu3, LU Ji-yuan1, LI Lei2
1. School of Internet Finance and Information Engineering, Guangdong University of Finance, Guangzhou, Guangdong 510521, China;
2. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong 510006, China;
3. School of Economics and Commerce, South China University of Technology, Guangzhou, Guangdong 510006, China
Abstract: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.
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