*The National Natural Science Foundation of China under Grant Nos.61402012，61572034(国家自然科学基金);the Natural Science Foundation of Universities in Anhui Province under Grant No.KJ2014A061(安徽省高校自然科学基金);the Major Science and Technology Projects in Anhui Province under Grant No.180********(安徽省重大科技专项).
Received 2017-11-08,Accepted 2018-01-30.
CNKI 网络出版:2018-02-06,https://www.doczj.com/doc/ae6857097.html,/KCMS/detail/11.5602.TP.20180206.1137.006.html
计算机科学与探索
Journal of Frontiers of Computer Science and Technology 局部差分隐私约束的链接攻击保护*
杨高明，方贤进+，肖亚飞
安徽理工大学计算机科学与工程学院，安徽淮南232001
+通讯作者E-mail:xjfang@https://www.doczj.com/doc/ae6857097.html,
摘要：传统意义的交互式差分隐私保护模型对数据查询结果进行扰动，不能满足用户对数据的多样化需求。为有效使用数据并满足隐私保护要求，用局部差分隐私的思想，在随机响应的基础上实现数据集的链接攻击保护。首先，针对原始数据的分布情况，研究如何更好地选择随机转换矩阵P ，在数据效用和隐私保护的基础上更好地实现链接隐私保护，从而避免身份披露和属性披露；其次，针对敏感、准标识符属性以及它们之间的组合讨论相应的隐私保护方法和数据效用的最大化，并给出数据扰动算法；最后，在已知数据分布均值和方差的基础上实验验证原始数据和扰动数据之间的KL-散度、卡方。实验结果表明所用随机化可以带来较小的效用损失。
关键词：局部差分隐私；随机响应；链接攻击；隐私保护
文献标志码：A 中图分类号：TP391
杨高明,方贤进,肖亚飞.局部差分隐私约束的链接攻击保护[J].计算机科学与探索,2019,13(2):251-262.
YANG G M,FANG X J,XIAO Y F.Local differential privacy against link attack[J].Journal of Frontiers of Computer Science and Technology,2019,13(2):251-262.
Local Differential Privacy Against Link Attack *
YANG Gaoming,FANG Xianjin +,XIAO Yafei
School of Computer Science and Engineering,Anhui University of Science and Technology,Huainan,Anhui 232001,China
Abstract:The traditional interactive differential privacy model perturbs the data query results,which cannot satisfy the users ’diverse needs for data.In order to effectively use the data and meet the privacy protection requirements,this paper uses the idea of local differential privacy to realize the link attack protection for the data set on the basis of random response.Firstly,this paper studies how to choose the random conversion matrix P better according to the distribution of the original data,to better realize the link privacy protection and to achieve the balance between data utility and privacy protection,so as to avoid the identity disclosure and attribute disclosure.Secondly,for sensitive 1673-9418/2019/13(02)-0251-12doi:10.3778/j.issn.1673-9418.1711017万方数据