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Shearing based data transformation approach for privacy preserving clustering

机译:基于剪切的隐私保留群集的数据转换方法

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Data mining is a technique used to extract un-known patterns from large volume of data. Privacy has be-come a major concern in data mining. The data which are stored in the organization's database may have some confi-dential information and hence it has to be protected from any unauthorized usage. In order to overcome the privacy problem in this paper we propose a new technique called shearing based composite transformation. Data owner trans-forms the original data into distorted data by shearing based composite data transformation. Only this distorted data is given to the clients. For clustering we have used k-means algorithm and from our experiments we found that the total number of elements in the clusters is same with the original and distorted data.
机译:数据挖掘是一种用于从大量数据中提取未知模式的技术。 隐私已成为数据挖掘的主要问题。 存储在组织的数据库中的数据可能具有一些混合信息,因此必须免受任何未经授权的用途保护。 为了克服本文中的隐私问题,我们提出了一种称为剪切基于复合转换的新技术。 数据所有者通过剪切基于的复合数据转换将原始数据转换为失真数据。 只有这种扭曲的数据被给予客户端。 对于群集,我们使用了K-Means算法,并从我们的实验中发现,我们发现群集中的元素总数与原始和扭曲的数据相同。

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