基于敏感信息邻近抵抗的匿名方法  

Anonymity Method Based on Proximity Resistance to Sensitive Information

作　　者：桂琼[1,2] 吕永军 程小辉 GUI Qiong;Lü Yongjun;CHENG Xiaohui(College of Information Science and Engineering,Guilin University of Technology,Guilin,Guangxi 541004,China;School of Information Engineering,Wuhan University of Technology,Wuhan 430070,China)

机构地区：[1]桂林理工大学信息科学与工程学院,广西桂林541004 [2]武汉理工大学信息工程学院,武汉430070

出　　处：《计算机工程》2020年第12期142-149,184,共9页Computer Engineering

基　　金：国家自然科学基金地区科学基金项目(61862019);广西自然科学基金面上项目(2017GXNSFAA198223)。

摘　　要：针对相似性攻击造成隐私泄露的问题,构建一种(r,k)-匿名模型,基于敏感属性语义关联,设定邻近抵抗阈值r,并提出满足该模型的匿名方法GDPPR。采用模糊聚类技术完成簇的划分,结合敏感属性相异度得出距离矩阵,使得每个等价类中相邻语义下的敏感属性取值频率不高于阈值r,同时保证较高的数据可用性。在两个标准数据集上的实验结果表明,该方案能够较好地满足(r,k)-匿名模型,有效抵抗相似性攻击,减少泛化产生的信息损失。In view of the problem of the privacy leakage caused by similarity attacks,this paper proposes a(r,k)-anonymous model.Based on the semantic association between sensitive attributes,the proximity resistance threshold r is set,and an anonymous method Generalized Data for Privacy Proximity Resistance(GDPPR)that satisfies the model is designed.The fuzzy clustering technique is used to complete the cluster partitioning,and the distance matrix is obtained by combining the dissimilarity of sensitive attributes.Therefore,the frequency of taking values of sensitive attributes under the proximity semantics in each equivalence class is kept under the threshold r and the data availability is ensured.Experimental results on two standard datasets show that GDPPR can satisfy the(r,k)-anonymity model.It effectively resists similarity attacks,and reduces the information loss caused by generalization.

关 键 词：数据匿名 相似性攻击 模糊聚类 邻近抵抗 数据泛化 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]