Improved collaborative filtering recommendation algorithm based on differential privacy protection

被引：64

作者：

Yin, Chunyong ^{[1
]}

Shi, Lingfeng ^{[1
]}

Sun, Ruxia ^{[1
]}

Wang, Jin ^{[2
]}

机构：

[1] Nanjing Univ Informat Sci & Technol, Jiangsu Engn Ctr Network Monitoring, Sch Comp & Software, Nanjing 210044, Peoples R China

[2] Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha 410004, Peoples R China

来源：

JOURNAL OF SUPERCOMPUTING | 2020年 / 76卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Collaborative filtering; Differential privacy; DiffGen; Time factor;

D O I：

10.1007/s11227-019-02751-7

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In order to receive efficient personalized recommendation, users have to provide personal information to service providers. However, in this process, personal private data are in an extremely dangerous situation. Personalized recommendation technology based on privacy protection can enable users to enjoy personalized recommendations, while private data are also protected. In this paper, an efficient privacy-preserving collaborative filtering algorithm is proposed, which is based on differential privacy protection and time factor. The proposed method used the MovieLens data set in the experiment. Experimental results showed that the proposed method can effectively protect the private data, but the accuracy of recommendation is slightly inferior than the traditional collaborative filtering algorithm.

引用

页码：5161 / 5174

页数：14

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