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An efficient approach for privacy preserving distributed mining of association rules in unsecured environment

机译:在不安全的环境中有效保护隐私的关联规则的分布式挖掘的有效方法

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Distributed data mining techniques are widely used for many applications viz; marketing, decision making, statistical analysis etc. In distributed data environment, each of the involving sites contains local information which will be collaborated to extract global mining result. However, these techniques have been investigated in terms of privacy and security concerns of individual site's information. To solve this problem, many cryptography techniques have been investigated. Still there is a room for further improvement. In this paper, we propose an efficient approach for privacy preserving distributed association rule mining. We use onion routing protocol in order to exchange information among involving sites. We use an elliptic curve (EC) based cryptography in order to achieve security and privacy of individual site's information in unsecured distributed environment. Finally, we analyze proposed solution in terms of security, privacy, computational cost and communication cost.
机译:分布式数据挖掘技术被广泛用于许多应用程序中。市场营销,决策,统计分析等。在分布式数据环境中,每个涉及的站点都包含本地信息,这些信息将被协作以提取全球采矿结果。但是,已针对各个站点信息的隐私和安全问题对这些技术进行了研究。为了解决这个问题,已经研究了许多密码技术。仍有进一步改进的空间。在本文中,我们提出了一种用于隐私保护的分布式关联规则挖掘的有效方法。我们使用洋葱路由协议以便在涉及的站点之间交换信息。我们使用基于椭圆曲线(EC)的加密技术,以在不安全的分布式环境中实现单个站点信息的安全性和私密性。最后,我们从安全性,隐私,计算成本和通信成本方面分析了提出的解决方案。

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