摘要
将发布的数据用于微观数据表包含的敏感属性分析,同时保持个人隐私,是一个越来越重要的问题。当前,k-匿名模型用于保护隐私数据公布,然而当以身份公开为重点时,k-匿名模型在某种程度上并不能保护属性公开。基于此,提出了一种新的基于(p+,α)-敏感k-匿名隐私保护模型,敏感属性首先通过其敏感性进行分类,然后发布敏感属性归属的类别。与以往增强k-匿名模型不同,该模型允许发布更多的信息,但不会影响隐私。实验结果表明,新提出的模型可以显著降低违反保密性。
Publishing data for analysis from a microdata table containing sensitive attributes,while maintaining individual pri-vacy,is a problem of increasing significance.Now,the k-anonymity model was proposed for privacy preserving data publica-tion.While focusing on identity disclosure,k-anonymity model fails to protect attribute disclosure to some extent.This paper proposed a new privacy protection model called （p＋,α）-sensitive k-anonymity,where sensitive attributes were first parti-tioned into categories by their sensitivity,and then published the categories that sensitive attributes belong to.Different from previous enhanced k-anonymity models,this model allowed us to release a lot more information without compromising privacy. Experimental results show that this introduced model can significantly reduce the privacy breach.
出处
《计算机应用研究》
CSCD
北大核心
2014年第11期3465-3468,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(61371113)
江苏省高校自然科学研究资助项目(14KJB520026)
