Privacy-Preserving Data Publishing for Multiple Numerical Sensitive Attributes  

作　　者：Qinghai Liu Hong Shen Yingpeng Sang 

机构地区：[1]School of Computer and Information Technology, Beijing Jiaotong University [2]School of Information Science and Technology, Sun Yat-sen University [3]School of Computer Science, University of Adelaide

出　　处：《Tsinghua Science and Technology》2015年第3期246-254,共9页清华大学学报（自然科学版（英文版）

基　　金：supported by the National Natural Science Foundation of China (No. 61170232);the 985 Project Funding of Sun Yat-sen University;State Key Laboratory of Rail Traffic Control and Safety Independent Research (No. RS2012K011);Ministry of Education Funds for Innovative Groups (No. 241147529)

摘　　要：Anonymized data publication has received considerable attention from the research community in recent years. For numerical sensitive attributes, most of the existing privacy-preserving data publishing techniques concentrate on microdata with multiple categorical sensitive attributes or only one numerical sensitive attribute. However, many real-world applications can contain multiple numerical sensitive attributes. Directly applying the existing privacy-preserving techniques for single-numerical-sensitive-attribute and multiple-categorical-sensitive- attributes often causes unexpected disclosure of private information. These techniques are particularly prone to the proximity breach, which is a privacy threat specific to numerical sensitive attributes in data publication, in this paper, we propose a privacy-preserving data publishing method, namely MNSACM, which uses the ideas of clustering and Multi-Sensitive Bucketization （MSB） to publish microdata with multiple numerical sensitive attributes. We use an example to show the effectiveness of this method in privacy protection when using multiple numerical sensitive attributes.Anonymized data publication has received considerable attention from the research community in recent years. For numerical sensitive attributes, most of the existing privacy-preserving data publishing techniques concentrate on microdata with multiple categorical sensitive attributes or only one numerical sensitive attribute. However, many real-world applications can contain multiple numerical sensitive attributes. Directly applying the existing privacy-preserving techniques for single-numerical-sensitive-attribute and multiple-categorical-sensitive- attributes often causes unexpected disclosure of private information. These techniques are particularly prone to the proximity breach, which is a privacy threat specific to numerical sensitive attributes in data publication, in this paper, we propose a privacy-preserving data publishing method, namely MNSACM, which uses the ideas of clustering and Multi-Sensitive Bucketization （MSB） to publish microdata with multiple numerical sensitive attributes. We use an example to show the effectiveness of this method in privacy protection when using multiple numerical sensitive attributes.

关 键 词：PRIVACY-PRESERVING K-ANONYMITY numerical sensitive attribute CLUSTERING Multi-Sensitive Bucketization（MSB） 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]