差分隐私保护k-means聚类方法研究  

Research on Differential Privacy Preserving k-means Clustering

作　　者：李杨[1,2] 郝志峰[2] 温雯[2] 谢光强[1,2] 

机构地区：[1]广东工业大学自动化学院,广州510006 [2]广东工业大学计算机学院,广州510006

出　　处：《计算机科学》2013年第3期287-290,共4页Computer Science

基　　金：国家自然科学基金(61070033);广东省自然科学基金(9251009001000005);广东省科技计划项目(2010B050400011)资助

摘　　要：研究了基于差分隐私保护的k-means聚类隐私保护方法。首先介绍了隐私保护数据挖掘和隐私保护聚类分析的研究现状,简单介绍了差分隐私保护的基本原理和方法。为了解决差分隐私k-means聚类方法聚类结果可用性差的问题,提出了一个新的IDP k-means聚类方法,并证明了其满足ε-差分隐私保护。最后的仿真实验表明,在相同隐私保护级别下,IDP k-means聚类方法与差分隐私k-means聚类方法相比,聚类可用性得到了较大程度的提高。We studied k-means privacy preserving clustering method within the framework of differential privacy. We first introduced the research status of privacy preserve data mining and privacy preserve clustering, briefly presenting the basic principle and method of differential privacy. To improve the poor clustering availability of differential privacy k-means,we presented a new method of IDP k-means clustering and proved it satisfies e-differential privacy. Our experi- ments show that at the same level of privacy preserve, IDP k-means clustering gets a much higher clustering availability than differential privacy k-means clustering method.

关 键 词：差分隐私 K-均值 聚类 隐私保护 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]