A Chaos-based Multiplicative Perturbation Scheme for Privacy Preserving Data Mining

被引：0
|
作者：
Luo, Zhifeng [1 ]
Wen, Congmin [1 ]
机构：
[1] S China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510641, Guangdong, Peoples R China
来源：
2014 5TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS) | 2014年
关键词：
privacy preserving data mining; multiplicative perturbation; logistic map; K-ANONYMITY;
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
The multiplicative perturbation is a popular scheme for privacy preserving data mining. It transforms the original data with the projection matrix. The security of projection matrix is a main concern in the multiplicative perturbation scheme. In this paper, we propose a novel multiplicative perturbation scheme which has a large key space. And we utilize the special property of chaotic systems, i.e., sensitivity to the initial condition and parameter, to design a new projection matrix generation algorithm. The experiment results show that the proposed scheme can preserve the privacy and maintain the utility for data miming.
引用
收藏
页码：941 / 944
页数：4
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