信息网络安全 ›› 2015, Vol. 15 ›› Issue (8): 53-58.doi: 10.3969/j.issn.1671-1122.2015.08.009

• 技术研究 • 上一篇    下一篇

一种基于网格聚类的查询隐私匿名算法研究

张付霞(), 蒋朝惠   

  1. 贵州大学计算机科学与技术学院,贵州贵阳 550025
  • 收稿日期:2015-05-12 出版日期:2015-08-01 发布日期:2015-08-21
  • 作者简介:

    张付霞(1987-),女,河南,硕士研究生,主要研究方向:计算机应用技术;蒋朝惠(1965-),男,四川,教授,硕士,主要研究方向:通信网络与信息安全。

  • 基金资助:
    贵州省科学技术基金[黔科合J字{2012}2128号];贵州大学研究生创新基金[校研理工2015017]

Research on Query Privacy Anonymity Algorithm Based on Grid Clustering

ZHANG Fu-xia(), JIANG Chao-hui   

  1. College of Computer Science and Technology, Guizhou University, Guiyang Guizhou 550025, China
  • Received:2015-05-12 Online:2015-08-01 Published:2015-08-21

摘要:

目前,大多数位置匿名聚类算法的每一次迭代都需要遍历所有用户的位置来寻找离匿名框质心最近的用户,这消耗了大量的时间,且对包含更多隐私信息的查询隐私没有做到更好的保护。为解决这些问题,文章提出基于网格聚类的查询隐私匿名算法(QPAGC)。该算法以网格作为处理单元,用户的位置可以定位到网格当中,从而不必遍历每个用户的具体位置;以目标用户所在网格求匿名框的质心和该匿名框所有邻域网格的质心,在所有邻域网格质心中选取离匿名框质心距离最小的邻域网格加入匿名框,直至匿名框的质量满足k-匿名要求;通过增加假用户和假请求使得匿名框满足p敏感约束以达到保护用户查询信息的目的。对比实验表明,在满足用户个性化要求下,基于网格聚类的查询隐私匿名算法匿名成功率更高,匿名区域面积更小,且提高了相对匿名度和用户的查询服务质量,从而平衡了隐私保护安全系数k和QoS之间的矛盾。

关键词: 位置服务, 网格聚类, k-匿名, 质心, 查询隐私

Abstract:

Currently, the each iteration of most location anonymous clustering algorithms is required to traverse all users’ locations to find the nearest user from the centroid of the anonymous box, which consumes a lot of time and does not provide better protection to query privacies that contain more sensitive information. To solve these problems, this paper proposes a query privacy anonymity algorithm based on grid clustering (QPAGC). The algorithm regards grid as the processing unit and all users’ positions can be located to a grid, which does not traverse the specific location of each user. The algorithm calculates the centroid of anonymous box and centroids of all the neighborhood grids, the neighborhood grid which its centroid is nearest from the centroid of anonymous box is added to anonymous box until the quality of anonymous box satisfies the requirement of k-anonymity constraint. Anonymous box satisfies p-sensitive constraint by adding fake users and fake requests to protect user’s query privacy. Contrast experiment shows that query privacy anonymity algorithm based on grid clustering has a higher success rate of anonymity and a smaller anonymous area, increases relative anonymity and the quality of the user’s query service by meeting the requirements of individual user, so the method balances the contradiction between the safety factor of k and QoS.

Key words: location-based service, grid clustering, k-anonymity, centroid, query privacy

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