School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China.
Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China.
Sensors (Basel). 2021 Mar 12;21(6):2021. doi: 10.3390/s21062021.
Recent years have seen the wide application of Location-Based Services (LBSs) in our daily life. Although users can enjoy many conveniences from the LBSs, they may lose their trajectory privacy when their location data are collected. Therefore, it is urgent to protect the user's trajectory privacy while providing high quality services. Trajectory -anonymity is one of the most important technologies to protect the user's trajectory privacy. However, the user's attributes are rarely considered when constructing the -anonymity set. It results in that the user's trajectories are especially vulnerable. To solve the problem, in this paper, a Spatiotemporal Mobility (SM) measurement is defined for calculating the relationship between the user's attributes and the anonymity set. Furthermore, a trajectory graph is designed to model the relationship between trajectories. Based on the user's attributes and the trajectory graph, the SM based trajectory privacy-preserving algorithm (MTPPA) is proposed. The optimal -anonymity set is obtained by the simulated annealing algorithm. The experimental results show that the privacy disclosure probability of the anonymity set obtained by MTPPA is about 40% lower than those obtained by the existing algorithms while the same quality of services can be provided.
近年来，基于位置的服务（LBS）在我们的日常生活中得到了广泛的应用。虽然用户可以从 LBS 中享受到许多便利，但他们的位置数据被收集时可能会失去轨迹隐私。因此，在提供高质量服务的同时保护用户的轨迹隐私是当务之急。轨迹匿名化是保护用户轨迹隐私的最重要技术之一。然而，在构建匿名集时很少考虑用户的属性。这导致用户的轨迹特别容易受到攻击。为了解决这个问题，本文定义了一个时空移动性（SM）度量标准，用于计算用户属性和匿名集之间的关系。此外，还设计了一个轨迹图来建模轨迹之间的关系。基于用户属性和轨迹图，提出了基于 SM 的轨迹隐私保护算法（MTPPA）。通过模拟退火算法得到最优的匿名集。实验结果表明，MTPPA 获得的匿名集的隐私泄露概率比现有算法低约 40%，同时可以提供相同质量的服务。
