Privacy Preserving Attribute Reduction for Horizontally Partitioned Data
There has been concern over the apparent conflict between privacy and data mining. Attribute reduction is one of the most important contributions of rough set theory to data mining. In this paper, we address the issue of privacy preserving attribute reduction. Specifically, we consider a scenario in which two parties owning private data, wish to run a attribute reduction algorithm on the union of their databases, without revealing information about individuals. Our work is motivated by the need both to protect private information and to enable its use for research or other purposes. The above problem is a specific example of secure multi-party computation. We focus on the problem with the attribute reduction algorithm based on relative granularity, address an efficient protocol for securely computing the relative granularity, and present a privacy preserving attribute reduction algorithm for horizontally partitioned data.
Mingquan Ye Xuegang Hu Changrong Wu
Institute of Computer and Information, Hefei University of Technology, HeFei 230009,China Computer S Institute of Computer and Information, Hefei University of Technology, HeFei 230009,China Institute of Mathematics and Computer, Anhui Normal University, WuHu 241002,China
国际会议
The 2010 International Conference on Intelligent Systems and Knowledge Engineering(第五届智能系统与知识工程国际会议)
杭州
英文
315-319
2010-11-15（万方平台首次上网日期，不代表论文的发表时间）