面向位置服务的K-Vretr隐私保护方法

K-Vretr Privacy Protection Method for Location-based Services

针对K匿名、空间泛化等隐私保护方法中匿名区域受攻击造成用户隐私泄露的问题,提出一种基于K匿名机制的K-Vretr方法.首先,引入Voronoi图模型,利用离散的Voronoi图特性,分析同类信息点,生成K匿名集发送给LBS服务器;其次,定义关系矩阵,计算出用户位置与目标信息点之间的邻近关系;再次,应用二次剩余假设模型,确保用户目标信息点的查询隐私安全;最后,通过实验验证K-Vretr方法在满足l-多样性的同时,既增大了匿名空间,又减少了匿名时间,进而保证了安全性与匿名效率,有效防止了用户隐私的泄露.

A K-Vretr method based on the K-anonymity mechanism is proposed to address the problem of user privacy leakage caused by attacks on anonymous regions in privacy protection methods such as K-anonymity and spatial generalization.Firstly, the Voronoi graph model is introduced, and the discrete Voronoi graph feature is utilized to analyze similar information points and generate a K-anonymous set to send to the LBS server;Secondly, the relationship matrix is defined to calculate the proximity relationship between the user location and the target information point;Again, the quadratic residual hypothesis model is applied to ensure the privacy security of the query of the user′s target information point;Finally, the K-anonymity mechanism is experimentally verified.The K-Vretr method increases the anonymity space and decreases the anonymity time while satisfying the l-diversity, thus guaranteeing security and anonymity efficiency and effectively preventing the leakage of user privacy.