模糊t-closeness隐私保护方法研究  

FUZZY T-CLOSENESS PRIVACY PROTECTION METHOD

作　　者：陈晓宇 韩斌 黄树成 朱文正 Chen Xiaoyu;Han Bin;Huang Shucheng;Zhu Wenzheng(School of Computer Science and Engineering,Jiangsu University of Science and Technology,Zhenjiang 212000,Jiangsu,China)

机构地区：[1]江苏科技大学计算机学院,江苏镇江212000

出　　处：《计算机应用与软件》2018年第9期317-322,333,共7页Computer Applications and Software

摘　　要：针对t-closeness隐私保护方法中数据泛化机制带来的信息损失量较大的不足,基于模糊理论提出一种模糊t-closeness隐私保护方法。该方法给出数据模糊化的定义,设计并实现替代泛化机制的数据模糊化过程。该过程在模糊聚类的基础上划分出符合t-closeness隐私保护要求的模糊等价类,对元素隶属度限幅求取平均值以获取元素模糊化的替代值。同时针对元素中分类属性给出模糊化语义结构树的构造方法。进行理论证明并通过实验验证模糊t-closeness隐私保护方法降低信息损失量的同时,提高了数据的隐私保护强度。Considering the shortcomings that the generalization mechanism of data lead to a lot of information loss in the t-closeness privacy protection method, this paper proposed a fuzzy t-closeness privacy protection method based on fuzzy theory. The method gave the definition of data blurring, designed and implemented data blurring instead of generalization mechanism. Based on fuzzy clustering, this process divided fuzzy equivalence classes according to t-closeness privacy protection requirements, and the average value of element membership limit was calculated to get the substitution value of element blurring. At the same time, the construction method of fuzzy semantic structure tree was given for categorical attributes of elements. Theoretical proof and experiments verify that the fuzzy t-closeness privacy protection method reduces the amount of information loss while enhancing the privacy protection strength of data.

关 键 词：t—closeness 隐私保护 模糊理论 模糊化 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]