Privacy-preserving collaborative filtering using randomized perturbation techniques

被引：0

作者：

Polat, H ^{[1
]}

Du, WL ^{[1
]}

机构：

[1] Syracuse Univ, Dept Elect Engn & Comp Sci, Syst Assurance Inst, Syracuse, NY 13244 USA

来源：

THIRD IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS | 2003年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Collaborative Filtering (CF) techniques are becoming increasingly popular with the evolution of the Internet. To conduct collaborative filtering, data from customers are needed. However, collecting high quality data from customers is not an easy task because man), customers are so concerned about their privacy that they might decide to give false information. We propose a randomized perturbation (RP) technique to protect users' privacy while still producing accurate recommendations.

引用

页码：625 / 628

页数：4

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