抵制敏感属性相似性攻击的(p,k,d)-匿名模型  

(p,k,d)-Anonymous Model for Resisting Sensitive Attributes Similarity Attack

作　　者：贾俊杰 陈露婷 

机构地区：[1]西北师范大学计算机科学与工程学院,兰州730070

出　　处：《计算机工程》2018年第3期132-137,共6页Computer Engineering

基　　金：兰州市科技计划项目(20141256)

摘　　要：针对当前p-Sensitive k-匿名模型未考虑敏感属性语义相似性,不能抵制相似性攻击的问题,提出一种可抵制相似性攻击的(p,k,d)-匿名模型。根据语义层次树对敏感属性值进行语义分析,计算敏感属性值之间的语义相异值,使每个等价类在满足k匿名的基础上至少存在p个满足d-相异的敏感属性值来阻止相似性攻击。同时考虑到数据的可用性,模型采用基于距离的度量方法划分等价类以减少信息损失。实验结果表明,提出的(p,k,d)-匿名模型相对于p-Sensitive k-匿名模型不仅可以降低敏感属性泄露的概率,更能有效地保护个体隐私,还可以提高数据可用性。Aiming at the problem that the current p-Sensitive k-anonymous model has no regard for the semantic similarity of sensitive attribute and can be susceptible to similarity attack, this paper proposes a （p, k, d）-anonymous model that can resist similarity attack. This model uses semantic hierarchical tree to semantic analysis of sensitive attribute values and computes the semantic dissimilar values between sensitive attribute values,and each equivalence class exist at least p-Sensitive attribute values that satisfy the d-different on the basis of satisfying k anonymity to prevent similarity attack. Considering the availability of data, the model divides the equivalence class by means of the distance-based measurement methods to reduce the loss of information. Experimental results show that compared with p-Sensitive k- anonymous model, the proposed （p, k, d）-anonymous model cannot only reduce the probability of sensitive attribute leakage to protect individual privacy more effectively, but also improve the usability of data.

关 键 词：数据发布 隐私保护 p—Sensitive K-匿名模型 (p k  d)-匿名模型 相似性攻击 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]