基于维诺图的位置隐私最近邻查询方法

针对K-匿名、空间匿名、位置模糊等隐私保护方法易受推理攻击及连续多查询攻击的不足,提出了-种抗 连续多查询攻击的基于维诺图的位置隐私最近邻查询算法.该算法基于K-匿名思想以及维诺图算法,在可信第三 方生成K-匿名集,用基于位置的服务（location based service, LBS）运营方服务器上存储的兴趣点（ point of interest, POI）划分维诺图,基于用户与POI之间的邻近关系生成关系矩阵;用K-匿名集生成的离散维诺图构成匿名空间, 以抵抗多查询攻击,保护用户位置隐私安全;用私有信息检索（ privacy information retrieval, PIR）技术保...

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Bibliographic Details
Published in北京工业大学学报 Vol. 44; no. 2; pp. 225 - 233
Main Author 周艺华;杜建航;杨宇光;侍伟敏
Format Journal Article
LanguageChinese
Published 北京工业大学信息学部,北京,100124 01.02.2018
Subjects
位置K-匿名
基于位置服务
私有信息检索
移动通信
随机匿名
隐私保护
基于位置服务
位置K-匿名
mobile communication
private information retrieval
privacy protection
随机匿名
隐私保护
私有信息检索
random anonymity
location-based service
移动通信
location K-anonymous
Online AccessGet full text
ISSN0254-0037
DOI10.11936/bjutxb2017040016

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Summary:针对K-匿名、空间匿名、位置模糊等隐私保护方法易受推理攻击及连续多查询攻击的不足,提出了-种抗 连续多查询攻击的基于维诺图的位置隐私最近邻查询算法.该算法基于K-匿名思想以及维诺图算法,在可信第三 方生成K-匿名集,用基于位置的服务（location based service, LBS）运营方服务器上存储的兴趣点（ point of interest, POI）划分维诺图,基于用户与POI之间的邻近关系生成关系矩阵;用K-匿名集生成的离散维诺图构成匿名空间, 以抵抗多查询攻击,保护用户位置隐私安全;用私有信息检索（ privacy information retrieval, PIR）技术保护用户兴趣 点查询隐私的安全.在保证关系矩阵匿名度的同时,也确保了K-匿名集的用户查询位置语义的单-性,以不同的 维诺图划分集合,确保了l-多样性.
Bibliography:ZHOU Yihua,DU Jianhang,YANG Yuguang,SHI Weimin （Faculty of Information Technology,Beijing University of Technology,Beijing 100124, China）
11-2286/T
location-based service; location K- anonymous; mobile communication; privacy protection; private information retrieval; random anonymity
With the rapid development of location services,the problem of privacy disclosure in location queries is becoming more and more severe. A variety of privacy protection algorithms came into being. To overcome the shortcomings of K- anonymous,spatial anonymity,location blur and other privacy protection methods,a location nearest neighbor query method based on Voronoi Mapagainst continuous multi-query attacks was proposed. Based on the K-anonymous idea and the Voronoi Map algorithm, the K-user anonymous set was generated by the trusted third party, and the Vino diagram was divided by the point of interest （ POI） stored in the LBS server. The relationship matrix was generated by the distance between the user and the POI. The discrete Vi
ISSN:0254-0037
DOI:10.11936/bjutxb2017040016