Privacy-preserving decision trees over vertically partitioned data

被引：0
|
作者：
Vaidya, J [1 ]
Clifton, C
机构：
[1] Rutgers State Univ, MSIS Dept, Newark, NJ 07102 USA
[2] Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA
来源：
DATA AND APPLICATIONS SECURITY XIX, PROCEEDINGS | 2005年 / 3654卷
关键词：
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. In this paper, we tackle the problem of classification. We introduce a generalized privacy preserving variant of the ID3 algorithm for vertically partitioned data distributed over two or more parties. Along with the algorithm, we give a complete proof of security that gives a tight bound on the information revealed.
引用
收藏
页码：139 / 152
页数：14
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