Privacy-preserving collaborative data mining

被引：0

作者：

Zhan, J ^{[1
]}

Chang, LW ^{[1
]}

Matwin, S ^{[1
]}

机构：

[1] Univ Ottawa, Sch Informat Technol & Engn, Ottawa, ON, Canada

来源：

FOUNDATIONS AND NOVEL APPROACHES IN DATA MINING | 2006年 / 9卷

关键词：

privacy; security; association rule mining; secure multi-party computation;

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：

Privacy-preserving data, mining is an important issue in the areas of data, mining. and secturity. In this paper, we study how to conduct association rule, mining, one of the core data, mining techniques, on private, data, in the following scenario: Multiple parties, each having a, private data set., want to jointly conduct association rule mining without disclosing their private data to other parties. Because of the interactive nature, among parties, developing a secure framework to achieve such a computation is both challenging and desirable. In this paper, we present; a secure, framework for multiple parties to conduct privacy-preserving association rule mining.

引用

页码：213 / +

页数：3

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