GA-based Approach for Hiding Sensitive Itemsets by Transaction Insertion

被引：0

作者：

Lin, Chun-Wei ^{[1
]}

Hong, Tzung-Pei ^{[2
,4
]}

Chang, Chia-Ching ^{[2
]}

Wang, Shyue-Liang ^{[3
]}

机构：

[1] Harbin Inst Technol, Shenzhen Grad Sch, Sch Comp Sci & Technol, IIIRC, Shenzhen 518055, Peoples R China

[2] Natl Univ Kaohsiung, Dept Comp Sci & Informat Engn, Kaohsiung 811, Taiwan

[3] Natl Univ Kaohsiung, Dept Informat Management, Kaohsiung 811, Taiwan

[4] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 804, Taiwan

来源：

2012 THIRD INTERNATIONAL CONFERENCE ON THEORETICAL AND MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE (ICTMF 2012) | 2013年 / 38卷

关键词：

Privacy preservation; Data mining; Genetic algorithm; Pre-large concept; Evolutionary 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：

Data mining technology is used to derive useful knowledge from large databases to aid corporate decision-making. The process of data collection and dissemination may, however, cause privacy concerns. Thus, privacy-preserving data mining (PPDM) has become an important issue. In this paper, an evolutionary privacy -preserving data mining method is proposed for finding appropriate itemsets within inserted transactions while hiding sensitive itemsets. An experiment is conducted to evaluate the performance of the proposed approach.

引用

页码：3 / 9

页数：7

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