基于连续查询的用户轨迹k-匿名隐私保护算法

k-anonymous privacy protection algorithm for user trajectory protection based on continuous query

针对用户轨迹隐私保护提出新的保护方法,该方法采用不可信第三方中心匿名器,用户获取自己的真实位置后首先在客户端进行模糊处理,然后提交给第三方匿名器,第三方匿名器根据用户的隐私需求结合用户某时刻的模糊位置信息生成虚假用户,然后根据历史数据生成虚假轨迹。为了进一步提高虚假轨迹与用户真实轨迹的相似性,该算法提出了虚假轨迹生成的两个约束条件:虚假轨迹距用户真实轨迹的距离约束和相似性约束。经大量实验证明,该算法与不同时刻k-匿名算法相比,不仅可以满足连续查询的用户轨迹隐私保护而且可以满足基于快照的LBS用户位置隐私保护。

This paper proposed a new protection method for user trajectory protection, which used untrusted third party server, users got real information about their location and blurred location information on the client side, and then submitted to the third party server. The third-party service in the user location generated true false user location according to the user＇s personalized privacy requirements and according to the historical data generated into a false track. In order to further improve the similarity between the false track and the user＇s true trajectory, this algorithm proposed two constraints： the distance constraint of the false track and the real trajectory of the user and similarity constraints. Compared with the traditional k-anonymous algo- rithm, the proposed algorithm not only can satisfy the user＇s trajectory privacy preserving of continuous query, but also can satisfy the user＇s location privacy protection based on snapshot LBS.