Protecting business intelligence and customer privacy while outsourcing data mining tasks

被引：0
|
作者：
Ling Qiu
Yingjiu Li
Xintao Wu
机构：
[1] James Cook University,School of Mathematics, Physics and Information Technology
[2] Singapore Management University,School of Information Systems
[3] University of North Carolina at Charlotte,Department of Software and Information Systems
来源：
Knowledge and Information Systems | 2008年 / 17卷
关键词：
False Positive Rate; Hash Function; Association Rule; False Negative Rate; Storage Requirement;
D O I：
暂无
中图分类号：
学科分类号：
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.
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页码：99 / 120
页数：21
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