Protecting business intelligence and customer privacy while outsourcing data mining tasks

被引：25

作者：

Qiu, Ling ^{[1
]}

Li, Yingjiu ^{[2
]}

Wu, Xintao ^{[3
]}

机构：

[1] James Cook Univ, Sch Math Phys & Informat Technol, Townsville, Qld 4811, Australia

[2] Singapore Management Univ, Sch Informat Syst, Singapore 178902, Singapore

[3] Univ N Carolina, Dept Software & Informat Syst, Charlotte, NC 28223 USA

来源：

KNOWLEDGE AND INFORMATION SYSTEMS | 2008年 / 17卷 / 01期

基金：

美国国家科学基金会;

关键词：

False Positive Rate; Hash Function; Association Rule; False Negative Rate; Storage Requirement;

D O I：

10.1007/s10115-007-0113-3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Nowadays data mining plays an important role in decision making. Since many organizations do not possess the in-house expertise of data mining, it is beneficial to outsource data mining tasks to external service providers. However, most organizations hesitate to do so due to the concern of loss of business intelligence and customer privacy. In this paper, we present a Bloom filter based solution to enable organizations to outsource their tasks of mining association rules, at the same time, protect their business intelligence and customer privacy. Our approach can achieve high precision in data mining by trading-off the storage requirement.

引用

页码：99 / 120

页数：22

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