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Fast Cryptographic Privacy Preserving Association Rules Mining on Distributed Homogenous Data Base

机译:快速加密隐私保留关联规则挖掘分布式同质数据库

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Privacy is one of the most important properties of an information system must satisfy. In which systems the need to share information among different, not trusted entities, the protection of sensible information has a relevant role. A relatively new trend shows that classical access control techniques are not sufficient to guarantee privacy when data mining techniques are used in a malicious way. Privacy preserving data mining algorithms have been recently introduced with the aim of preventing the discovery of sensible information. In this paper we propose a modification to privacy preserving association rule mining on distributed homogenous database algorithm. Our algorithm is faster than old one which modified with preserving privacy and accurate results. Modified algorithm is based on a semi-honest model with negligible collision probability. The flexibility to extend to any number of sites without any change in implementation can be achieved."And also any increase doesn't add more time to algorithm because all client sites perform the mining in the same time so the overhead in communication time only. The total bit-communication cost for our algorithm is function in (N) sites.
机译:隐私是信息系统最重要的属性必须满足。在哪些系统中,需要共享不同,不值得信赖的实体之间的信息,保护合理信息的保护具有相关的作用。相对较新的趋势表明,当以恶意方式使用数据挖掘技术时,经典访问控制技术不足以保证隐私。最近旨在防止发现可明智的信息的目的,最近推出了隐私保留数据挖掘算法。本文提出了对分布式同质数据库算法的隐私保留关联规则挖掘的修改。我们的算法比旧的算法更快地修改,以保留隐私和准确的结果。修改的算法基于半诚实模型,碰撞概率可忽略不计。可以实现无任何改变的任何数量的站点的灵活性。“并且还有任何增加都不会增加算法的更多时间,因为所有客户端站点同时执行挖掘所以仅在通信时间中的开销。我们算法的总比特通信成本在(n)站点中是函数。

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