基于几何变换随机响应法的隐私保护聚类

Jie Liu, Yifeng Xu
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引用次数: 26

摘要

随着数据挖掘技术和挖掘工具的大量涌入,对个人隐私的保密要求越来越迫切。因此,如何保证个人隐私并获得正确的挖掘结果成为一个亟待解决的问题。本文提出了一种随机响应的几何变换方法——随机响应技术与几何变换算法的结合。该算法旨在解决几何变换算法隐私保护程度低的不足。该算法首先给出四个参数,分别对应四种不同类型几何变换的概率。根据生成的各种随机数,选择不同的几何变换方法,起到保护隐私的双重作用。我们的实验证明,该方法具有高度的隐私保护,可以得到正确的挖掘结果。
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Privacy Preserving Clustering by Random Response Method of Geometric Transformation
With the large influx of the data mining technology and mining tools, the confidentiality requirements of the personal privacy are becoming more and more urgent. Therefore, how to ensure personal privacy and get the correct mining results becomes a severe issue to be resolved. In this paper, we propose a kind of random response method of geometric transformation- the combination of the random response technology and the geometric transform algorithm. The algorithm is designed to solve the shortage of low privacy protection of the geometric transform algorithm. The algorithm first gives four parameters, corresponding to the probability of four different types of geometric transformations. According to the various random number generated, different geometric transformation method is selected, which serves the dual effect of privacy protection. Our experiment proves that this method has a high degree of privacy protection and can get correct mining results.
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