College of Computer Science and Technology, Harbin Engineering University, Harbin, PR China.
College of Information Science and Electronic Technology, Jiamusi University, Jiamusi, PR China.
PLoS One. 2019 Jul 25;14(7):e0220278. doi: 10.1371/journal.pone.0220278. eCollection 2019.
As the location trajectory contains more spatial-temporal information about the user, it will be even dangerous for jeopardizing the privacy of the user. In order to cope with the correlation, an algorithm that utilizes the query division had been proposed. In this algorithm, random blocks of query context was used, so as the adversary was obfuscated and difficult to correlate the real result. However, this algorithm fails to dispose the size of each query block, as once same size blocks were obtained by the adversary continuously, so the adversary can regard them as blocks from the same query context, and then obtains the query context to correlate the discrete locations. In view of above conditions, in this paper we propose a fine granularity block division algorithm based on the conception of granularity measurement as well as granularity layer division, so with the help of collaborative users the location privacy of the user will be protected. In this algorithm, the query context will be divided into fine granularity size of information blocks that difficult to be distinguished with others, and then these blocks will be exchanged with other collaborative users to eliminate the difference in block size. In addition, as each block is divided into fine granularity size, the adversary will be difficult to correlate the discrete locations into location trajectory, so the location privacy will be protected. At last, through security analysis and experimental verification, this granularity indistinguishable algorithm is analyzed and verified at both theoretical and practical levels, which further demonstrate the superiority of the proposed algorithm compared with other similar algorithms.
由于位置轨迹包含更多关于用户的时空信息，因此可能会危及用户的隐私。为了应对这种相关性，已经提出了一种利用查询划分的算法。在该算法中，使用了随机查询上下文块，从而使攻击者感到困惑，难以关联真实结果。但是，该算法无法处理每个查询块的大小，因为一旦攻击者连续获得相同大小的块，他就可以将它们视为来自同一查询上下文的块，然后获取查询上下文以关联离散位置。鉴于上述情况，本文提出了一种基于粒度测量和粒度层划分概念的细粒度块划分算法，以帮助协作用户保护用户的位置隐私。在该算法中，查询上下文将被细分为难以与其他块区分的信息块，然后这些块将与其他协作用户交换，以消除块大小的差异。此外，由于每个块都细分为粒度块，攻击者很难将离散位置关联到位置轨迹中，因此可以保护位置隐私。最后，通过安全性分析和实验验证，从理论和实践两个方面对这种不可区分粒度算法进行了分析和验证，进一步证明了所提出算法相对于其他类似算法的优越性。
