Privacy in data mining

被引：15

作者：

Domingo-Ferrer, J

Torra, V

机构：

[1] Univ Rovira & Virgili, Dept Comp Engn & Math, E-43007 Tarragona, Catalonia, Spain

[2] CSIC, Inst Invest Intel Ligencia Artificial, E-08193 Bellaterra, Catalonia, Spain

来源：

DATA MINING AND KNOWLEDGE DISCOVERY | 2005年 / 11卷 / 02期

关键词：

Data Mining; Association Rule; Disclosure Risk; Statistical Privacy; Automate Data Collection;

D O I：

10.1007/s10618-005-0009-3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Authors views on combining computer science and statistics to foster the development of privacy-preserving data mining (PPDM) are described. In the first paper authors determined which PPDM techniques are best to protect sensitive information, and how the, quality and privacy measures must be defined. The second paper analyzes the problem of confidentiality in categorical statistical databases when association rules are to be preserved. The third paper proposes to use probabilities to define bounded information loss measures for any statistic of interest. The fourth paper deals with k-anonymity, which is a useful concept to manage the conflict between data quality and individual privacy.

引用

页码：117 / 119

页数：3