Achieve Personalized Anonymity Through Query Blocks Exchanging

作     者：Chunguang Ma Lei Zhang Songtao Yang Xiaodong Zheng Pinhui Ke 

作者机构：College of Computer Science and TechnologyHarbin Engineering University Fujian Provincial Key Laboratory of Network Security and CryptologyFujian Normal University College of Information and Electronic TechnologyJiamusi University College of Applied TechnologyQiqihar University 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2016年第13卷第11期

页      面：106-118页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0839[工学-网络空间安全] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China (Grant No.61472097) the Specialized Research Fund for the Doctoral Program of Higher Education(Grant No.20132304110017) the Natural Science Foundation of Heilongjiang Province of China (Grant No.F2015022) the Fujian Provincial Key Laboratory of Network Security and Cryptology Research Fund (Fujian Normal University) (No.15003) 

主　　题：cyberspace security location-based services collaborative users personalized anonymity query and location privacy 

摘      要：In cyberspace security,the privacy in location-based services(LBSs) becomes more critical. In previous solutions,a trusted third party(TTP) was usually employed to provide disturbance or obfuscation,but it may become the single point of failure or service bottleneck. In order to cope with this drawback,we focus on another important class,establishing anonymous group through short-range communication to achieve k-anonymity with collaborative users. Along with the analysis of existing algorithms,we found users in the group must share the same maximum anonymity degree,and they could not ease the process of preservation in a lower one. To cope with this problem,we proposed a random-QBE algorithm to put up with personalized anonymity in user collaboration algorithms,and this algorithm could preserve both query privacy and location privacy. Then we studied the attacks from passive and active adversaries and used entropy to measure user s privacy level. Finally,experimental evaluations further verify its effectiveness and efficiency.