Preserving Privacy in Mining Quantitative Associations Rules

被引：1

作者：

Ahluwalia, Madhu V. ^{[1
]}

Gangopadhyay, Aryya ^{[2
]}

Chen, Zhiyuan ^{[3
]}

机构：

[1] Univ Maryland Baltimore Cty, Baltimore, MD 21228 USA

[2] Univ Maryland Baltimore Cty, Informat Syst, Baltimore, MD 21228 USA

[3] Univ Maryland Baltimore Cty, Dept Informat Syst, Baltimore, MD 21228 USA

来源：

INTERNATIONAL JOURNAL OF INFORMATION SECURITY AND PRIVACY | 2009年 / 3卷 / 04期

基金：

美国国家科学基金会;

关键词：

Association Rule Mining; Discrete Wavelet Transform; Privacy Preserving Data Mining;

D O I：

10.4018/jisp.2009100101

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Association rule mining is an important data mining method that has been studied extensively by the academic community and has been applied in practice. In the context of association rule mining, the state-of-the-art in privacy preserving data mining provides solutions for categorical and Boolean association rules but not for quantitative association rules. This article fills this gap by describing a method based on discrete wavelet transform (DWT) to protect input data privacy while preserving data mining patterns for association rules. A comparison with an existing kd-tree based transform shows that the DWT-based method fares better in terms of efficiency, preserving patterns, and privacy.

引用

页码：1 / 17

页数：17

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