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Secure Association Rule Mining for Distributed Level Hierarchy in Web

机译:Web中分布式级别层次结构的安全关联规则挖掘

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Data mining technology can analyze massive data and it play very important role in many domains, if it used improperly it can also cause some new problem of information security. Thus several privacy preserving techniques for association rule mining have also been proposed in the past few years. Various algorithms have been developed for centralized data, while others refer to distributed data scenario. Distributed data Scenarios can also be classified as heterogeneous distributed data and homogenous distributed data and we identify that distributed data could be partitioned as horizontal partition (a.k.a. homogeneous distribution) and vertical partition (a.k.a. heterogeneous distribution). In this paper, we propose an algorithm for secure association rule mining for vertical partition.
机译:数据挖掘技术可以分析海量数据,并且在许多领域中都起着非常重要的作用,如果使用不当,还会引起一些新的信息安全问题。因此,在过去的几年中,已经提出了几种用于关联规则挖掘的隐私保护技术。已经针对集中式数据开发了各种算法,而其他算法则涉及分布式数据方案。分布式数据方案也可以分为异构分布数据和同质分布数据,我们确定分布式数据可以划分为水平分区(也称为同质分布)和垂直分区(也称为异构分布)。在本文中,我们提出了一种用于垂直分区的安全关联规则挖掘算法。

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