面向敏感性攻击的多敏感属性数据逆聚类隐私保护方法  

A Multi Sensitive Attribute Data Inverse Clustering Privacy Preserving Algorithm for Sensitivity Attack

作　　者：张冰[1] 杨静[1] 张健沛[1] 谢静[1] 

机构地区：[1]哈尔滨工程大学计算机科学与技术学院,黑龙江哈尔滨150001

出　　处：《电子学报》2014年第5期896-903,共8页Acta Electronica Sinica

基　　金：国家自然科学基金(No.61370083;No.61073043;No.61073041);高等学校博士学科点专项科研基金(No.20112304110011;No.20121204110012);哈尔滨市科技创新人才研究专项资金(优秀学科带头人)(No.2011RFXXG015)

摘　　要：针对传统l-多样性模型仅考虑等价类中敏感值形式上的差异,而忽略敏感值的敏感度差异,且难以抵御一种新的攻击方式———敏感性攻击的问题,提出了一种使用逆文档频率IDF度量敏感值的敏感度的方法,并使用属性分解的方式构造敏感组,以避免多敏感属性数据表的QI属性泛化造成的高信息损失.同时,还提出了一种面向敏感性攻击的多敏感属性(l1,l2,…,ld)-多样性隐私保护算法MICD,该算法通过敏感度的逆聚类实现敏感组中敏感值的敏感度差异,以提高多敏感属性数据表抵御敏感性攻击的能力.实验结果表明,MICD算法能够较好的抵御敏感性攻击,且具有较小的信息损失量.In allusion to l-diversity model not considering sensitivity differences between the sensitive attributes, a new attack pattern which named sensitivity attack was proposed. Secondly, a new sensitive groups constructing method which based on sensitive attributes decomposition was proposed,and a keyword weight evaluation method called IDF was used to measure the sensitivity of the sensitive values.At the same time,a multi sensitive atWibutes （ l1, l2,…,ld）-diversity privacy preserving method for sensitivity attack which called MICE） was proposed, which guaranteed the sensitivity difference between sensitive values in sensitive groups by sensitivity inverse clustering. Experiment results demonstrated that the MICD algorithm could better protect sensitive attributes a- gainst sensitivity attack, and more effective on information loss.

关 键 词：隐私保护 敏感性攻击 逆聚类 多敏感属性 (l1 l2 … ld)-多样性 敏感度差异 

分 类 号：TP309.2[自动化与计算机技术—计算机系统结构]