An efficient model and algorithm for privacy-preserving trajectory data publishing

Since Abul et al. first proposed the k-anonymity based privacy protection for trajectory data, the researchers have proposed a variety of trajectory privacy-preserving methods, these methods mainly adopt the static anonymity algorithm, which directly anonymize processing and data publishing after initialization. They do not take into account the real application scenarios of moving trajectory data. The objective of this paper is to realize the dynamic data publishing of high dimensional vehicle trajectory data privacy protection under (k, δ) security constraints. First of all, we propose the partition storage and calculation for trajectory data. According to the spatial and temporal characteristics of vehicle trajectory data, we choose the sample point (x₂, y₂, t) at the time t_i as partition fields, partition storage of the trajectory data according to the time sequence and the location of the running vehicle is (Formula Presented.). The computation of data scanning in trajectory data clustering and privacy processing is reduced greatly through this method. Secondly, the dynamic clustering method is used to cluster the regional data. According to the characteristics of the vehicle trajectory data, (Formula Presented.) as the release data identifier, trajectory attributes of the vehicle as the sensitive attributes, we use Data Partitioning and Cartesian Product (DPCP) method to cluster trajectory data under the (k, δ) security constraints. Thirdly, the anonymization function f_DPCP is used to preserve the privacy of clustering trajectory data. In each sampling time slice, f_DPCP function is used to generalize the location data in the grouping. Through the continuous algorithm optimization and the experimental verification of real trajectory data, this model and algorithm can effectively protect privacy under the security constraint of (k, δ). By means of data simulation and data availability evaluation, the data processed by the anonymization method has a certain usability under the threshold of δ. At the same time, the experimental results are compared with the classical NWA algorithm, and DLBG, the method in this paper have been proved to be advanced in time cost and data availability evaluation.