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

页码：941 / 944

页数：4

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