A NOVEL PRIVACY-PRESERVING ASSOCIATION RULES MINING METHOD

被引：1

作者：

Tian Hong ^{[1
]}

Wang Xiukun ^{[2
]}

机构：

[1] Dalian Jiaotong Univ, Software Inst, Dalian 116028, Peoples R China

[2] Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China

来源：

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | 2010年 / 24卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Preserving privacy; association rule; PRRPM (partial randomized response based on probability matrix);

D O I：

10.1142/S021800141000824X

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In order to mine association rules accurately and efficiently while preserving the privacy thereof, a novel privacy-preserving association rules mining method is proposed in this paper. Known as the partial randomized response based on probability matrix, or PRRPM, this method chooses different data transition strategies to find frequent 1-itemsets and k-itemsets (k > 1). The PRRPM algorithm is explored and its validity examined through theoretical analysis and experiments.

引用

页码：995 / 1009

页数：15

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