欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
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Qo S之间的矛盾。
英文摘要：
Currently,the each iteration of most location anonymous clustering algorithms is required to traverse all users＇ locations to fi nd 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 specifi c 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 Qo S.
同期刊论文项目
同项目期刊论文
期刊信息
位置：