基于属性相关性划分的多敏感属性隐私保护方法  

A Privacy Preserving Approach Based on Attributes Correlation Partition for Multiple Sensitive Attributes

作　　者：谢静[1] 张健沛[1] 杨静[1] 张冰[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9期1718-1723,共6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.20122304110012);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：近年来,基于l-多样性的多维敏感属性的隐私保护研究日趋增多,然而大部分多敏感属性隐私保护方法都是基于有损分解的思想,破坏了数据间的关系,降低了数据效用.为此,提出了一种面向多敏感属性的隐私模型,首先给出一种l-maximum原则用以满足多敏感属性l-多样性要求;其次,为了保护属性间的相关性,根据属性间的依赖度对属性进行划分;最后设计并实现了MSA l-maximum(Multiple Sensitive Attributes l-maximum)算法.实验结果表明,提出的模型在保护隐私不泄露的同时,减少了元组的隐匿率,并且保护了数据间的关系.In recent years ,l-diversity models are suitable not only for single sensitive attribute data tables ,but also for multi-ple sensitive attributes data tables .However ,most of the research is based on lossy join ,it breaks the relationship between data .To address these problems ,a model based on multiple sensitive attributes is proposed .The main idea of the model is that it proposes a l-maximum principle that can satisfy the multiple sensitive attributes l-diversity at first .Then ,to protect the relationship between da-ta ,the model partitions attributes by the dependency degree between attributes .Finally ,a multiple sensitive attributes l-maximum al-gorithm （MSA l-maximum ）is proposed .The experiment results show that the proposed model can preserve the security of sensitive data ,meanwhile it can also reduce the information hidden rate and keep a high data utility .

关 键 词：隐私保护 多敏感属性 l-多样性 属性相关性 划分 

分 类 号：TP309-2[自动化与计算机技术—计算机系统结构]