一种基于（p＋，α）-敏感 k-匿名的增强隐私保护模型

摘要

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.%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。