A Distributed Privacy-Preserving Association Rules Mining Scheme Using Frequent-Pattern Tree-猫眼课题宝

DOI：

10.1007/978-3-540-88192-6_17

发表时间：

2008-10

期刊：

影响因子：

通讯作者：

中科院分区：

其他

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作者：

Chunhua Su;K. Sakurai

关联规则挖掘是一种常用的技术，它发现大量数据项之间的有趣的关联和相关关系，这些数据项经常出现在一起。如今，数据收集在社会和商业领域无处不在。许多公司和组织希望进行协同关联规则挖掘，以获得共同的利益。然而，敏感信息的泄漏是一个问题，我们必须解决和隐私保护技术的强烈需要。针对关联规则挖掘中的隐私问题，提出了一种基于频繁模式树（FP-tree）的隐私保护方案，用于协同关联规则挖掘中隐私信息的保护。我们证明了我们的方案是安全的，并且对于n个参与者是抗共谋的，这意味着即使n-1个不诚实的参与者与一个不诚实的数据挖掘者勾结，试图学习诚实的受访者与他们的响应之间的关联规则，他们也无法成功。

Association rules mining is a frequently used technique which finds interesting association and correlation relationships among large set of data items which occur frequently together. Nowadays, data collection is ubiquitous in social and business areas. Many companies and organizations want to do the collaborative association rules mining to get the joint benefits. However, the sensitive information leakage is a problem we have to solve and privacy-preserving techniques are strongly needed. In this paper, we focus on the privacy issue of the association rules mining and propose a secure frequent-pattern tree (FP-tree) based scheme to preserve private information while doing the collaborative association rules mining. We show that our scheme is secure and collusion-resistant fornparties, which means that even ifn− 1 dishonest parties collude with a dishonest data miner in an attempt to learn the associations rules between honest respondents and their responses, they will be unable to success.

A Distributed Privacy-Preserving Association Rules Mining Scheme Using Frequent-Pattern Tree

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