基于近似匹配的假位置k-匿名位置隐私保护方法

为了提高假位置k-匿名位置隐私保护方法中的假位置生成效率和查询服务质量,以及解决假位置生成过程中预处理复杂、没有充分考虑地理语义信息特征等问题,提出一种基于近似匹配的假位置k-匿名位置隐私保护方法.首先,将所选区域划分为若干个正方形网格,并将各位置坐标按所在网格转换为莫顿码;然后,通过对各位置莫顿码之间的近似匹配,选取互不相邻、分布在不同网格的位置点,生成假位置候选集;最后,对候选集中位置点的地名信息进行近似匹配, 得到位置点之间的语义相似度, 并选取语义相似度最小的$k-1$个位置点作为假位置.实验结果表明,所提出的方法在保证假位置之间物理分散性和语义多样化的同时,能够提高假位置生成效率,有效平衡隐私保护效果和查询服务质量.

A k-anonymous location privacy protection method of dummy based on approximate matching

In order to improve the efficiency of dummy generation and the query service quality in the k-anonymous location privacy protection method of dummy, and to solve the problem of that the preprocessing is complex and the geographic semantic features are not fully considered in dummy location generation, a k-anonymous location privacy protection method of dummy based on approximate matching is proposed. Firstly, the area is divided into several square grids, and the coordinates of each location are converted to Morton code. Then, through the approximate matching between the Morton codes of different locations, a candidate set of dummies is generated, and the locations in it are non adjacent to each other and distributed in different grids. Finally, by matching approximately geographic names information of locations, the semantic similarity between any two locations in the candidate set is obtained, and $k-1$ locations with the minimum semantic similarity are selected as dummies. Experimental results show that the method can ensure the physical dispersion and semantic diversity, and can improve the efficiency of dummy generation. At the same time, the balance between privacy protection security and query service quality is achieved.