k -匿名敏感属性多样性研究

X. Ren, J. Yang, Fengmei Wei
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引用次数: 0

摘要

保护隐私的常用方法是在数据发布中使用k -匿名。本文综合分析了用于防止数据发布中隐私泄露的K-匿名模型的研究现状,研究了K-匿名敏感属性多样性的特点,提出了CBK(L,K)-匿名算法来解决数据发布中隐私信息泄露的问题,该算法能使匿名数据有效抵抗后台知识攻击和同质性攻击,并能解决敏感属性的多样性问题。此外,我们将在另一篇论文中扩展我们的思想,处理如何使用CBK(L,K)-匿名算法来解决隐私信息泄露问题。
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Research on Diversity of Sensitive Attribute of K-Anonymity
The common way to protect privacy is to use K-anonymity in data publishing. This paper will analyse comprehensively the current research situation of K-anonymity model used to prevent privacy leaked in data publishing, we study the characteristics of sensitive attribute diversity of K-Anonymity, and propose CBK(L,K)-Anonymity algorithm in order to solve the problem of privacy information leakage in publishing the data, it can make anonymous data effectively resist background knowledge attack and homogeneity attack , and can solve diversity of sensitive attribute. In addition, we will extend our ideas for handling how to solve privacy information leakage problem by using CBK(L,K)-Anonymity algorithm in another paper.
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