Abstract
With the rapid development of location-based service technology, the leakage of trajectory privacy has become more and more serious. In order to solve the problems of insufficient privacy protection and low availability of published data in the existing trajectory privacy protection models, we propose a spatiotemporal generalized trajectory data publishing algorithm SGTP based on differential privacy. Firstly, a spatiotemporal generalization method of trajectories based on clustering is designed. The temporal location set is divided by a density peak trajectory clustering algorithm (DPTC), and the location is probabilistically generalized combined with an exponential mechanism to hide the real location information of users. Secondly, random noise is added to the generalized trajectory statistics by the Laplace mechanism, and the noise is post-processed by consistency constraints to improve the utility of the published data without affecting the privacy of the trajectories. Finally, we theoretically demonstrate that SGTP strictly satisfies differential privacy. Experimental results based on publicly available data show that SGTP can effectively protect user privacy and guarantee data utility and at the same time has a higher execution efficiency.
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Acknowledgements
This work was supported by National Science and Technology Innovation 2030-Key Project of ”New Generation Artificial Intelligence” under Grant 2021ZD0113103.
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Qiu, S., Pi, D., Wang, Y. et al. SGTP: A Spatiotemporal Generalized Trajectory Publishing Method With Differential Privacy. J Ambient Intell Human Comput 14, 2233–2247 (2023). https://doi.org/10.1007/s12652-022-04481-w
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DOI: https://doi.org/10.1007/s12652-022-04481-w