Trajectory privacy protection method in road network environment

Abstract

The invention discloses a trajectory privacy protection method in a road network environment, and relates to the technical field of trajectory privacy protection methods. The method includes the following steps of trajectory data preprocessing, wherein original trajectory data is divided into equivalence class trajectories with the same trajectory cross-domain time quantum; trajectory graph construction, wherein the equivalence class trajectories are constructed into a trajectory graph according to the s-space overlapping concept; trajectory graph division, wherein the trajectory graph is divided into multiple subgraphs with the greedy algorithm, each subgraph contains at least k trajectory graph vertexes, and thus a trajectory k-anonymity set is just formed. According to the method, the trajectory data is simulated into an undirected graph, trajectory k-anonymity is achieved through k-node division of the graph, and the anonymity success rate is high; besides, the availability of data obtained after privacy protection is high.

Description

technical field [0001] The invention relates to the technical field of track privacy protection methods, in particular to a track privacy protection method in a road network environment. Background technique [0002] With the rapid development of mobile positioning technology, a large number of devices with mobile positioning functions have appeared, and a large amount of location data has also been generated. The location data is sorted by sampling time to form trajectory data. Analysis and mining of trajectory data can support a variety of mobile-related applications. However, trajectory data contains rich spatiotemporal information, and the analysis and mining of trajectory data may lead to the leakage of personal private information contained in trajectory data. [0003] With the increasing demand for trajectory data applications in all walks of life, trajectory privacy protection technology has become one of the research hotspots at home and abroad. Researchers have s...

AI Technical Summary

Problems solved by technology

The key problem of the sharding method is: how to determine the length of the trajectory segment - if the trajectory segment is too short, it is tantamount to position privacy protection and cannot achieve the effect of trajectory privacy protection; if the trajectory segment is too long, it will not achieve the effect of division

[0015] Most of the above studies are aimed at trajectory privacy protection technology in free space, but in road network space, trajectory data is limited by the road network model, and the previous privacy protection algorithms are no longer applicable

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