| Citation: | ZHANG X J，XU C，TIAN F，et al. Utility-enhanced synthesis method of differentially private trajectories[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（12）：3615-3631 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.1013
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Trajectory data is valuable for a variety of applications. However, it has been a long-standing challenge to share and utilize trajectory data while protecting users’ privacy. Currently, the prevailing shared trajectory privacy-preserving methods are to generate complete synthetic trajectories that are highly similar to real trajectories based on differential privacy, which results in poor utility of synthesis trajectories and is vulnerable to location privacy inference attacks. To address these problems, this paper proposed a utility-enhanced synthesis method of differentially private trajectories (UtiE-DPT). By dividing the real trajectory dataset spatially, this method constructed a fine-grained adaptive density grid structure to discretize the real trajectory and designed a Markov transition matrix, trajectory travel distribution, and trajectory length distribution calculation model suitable for the adaptive density grid structure, so as to extract key statistical features to maintain the utility of real trajectories and thus enhance the utility of synthetic trajectories. To preserve the users’ privacy, the differential privacy technique was employed to perturb these key statistical features. Finally, a synthetic trajectory against inference attacks was generated according to the extracted features and anti-attack constraint strategy. Comprehensive experiments on real datasets and simulation datasets show that compared with the existing trajectory synthesis privacy protection methods such as DP-Star and AdaTrace, the UtiE-DPT not only protects trajectory privacy and resists location privacy inference attacks but also improves the utility of synthetic trajectories. Without considering the inference attacks, the query error of UtiE-DPT for generating synthetic trajectories is 21%–27% lower than AdaTrace and 32%–53% lower than DP-Star. After resisting the inference attacks, although the robustness of the synthetic trajectory is reduced by about 1%–2% compared with AdaTrace, the query error is reduced by 16%–21% compared with AdaTrace, achieving a better balance between privacy protection and utility.
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ZHANG X J，XU C，TIAN F，et al. Utility-enhanced synthesis method of differentially private trajectories[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（12）：3615-3631 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.1013
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