A Privacy-Preserving Trajectory Publishing Method Based on Multi-Dimensional Sub-Trajectory Similarities

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 24; p. 9652
• Main Authors: Shen, Hua, Wang, Yu, Zhang, Mingwu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 06.12.2023; MDPI
• Subjects: Access to information; Data integrity; Information management; k-anonymity; Methods; Privacy; privacy preservation; Privacy, Right of; Publishing; Semantics; Social networks; trajectory privacy; trajectory publishing; trajectory privacy; trajectory publishing; privacy preservation; k-anonymity
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23249652

With the popularity of location services and the widespread use of trajectory data, trajectory privacy protection has become a popular research area. -anonymity technology is a common method for achieving privacy-preserved trajectory publishing. When constructing virtual trajectories, most existing trajectory -anonymity methods just consider point similarity, which results in a large dummy trajectory space. Suppose there are similar point sets, each consisting of points. The size of the space is then mn. Furthermore, to choose suitable - 1 dummy trajectories for a given real trajectory, these methods need to evaluate the similarity between each trajectory in the space and the real trajectory, leading to a large performance overhead. To address these challenges, this paper proposes a -anonymity trajectory privacy protection method based on the similarity of sub-trajectories. This method not only considers the multidimensional similarity of points, but also synthetically considers the area between the historic sub-trajectories and the real sub-trajectories to more fully describe the similarity between sub-trajectories. By quantifying the area enclosed by sub-trajectories, we can more accurately capture the spatial relationship between trajectories. Finally, our approach generates k-1 dummy trajectories that are indistinguishable from real trajectories, effectively achieving -anonymity for a given trajectory. Furthermore, our proposed method utilizes real historic sub-trajectories to generate dummy trajectories, making them more authentic and providing better privacy protection for real trajectories. In comparison to other frequently employed trajectory privacy protection methods, our method has a better privacy protection effect, higher data quality, and better performance.