Abstract:Trajectory data is fundamental to modern urban intelligence, yet its sensitivity raises significant privacy concerns. Generative models such as Generative Adversarial Networks, Variational Autoencoders, and Diffusion Models have been developed to generate realistic synthetic trajectory data by capturing underlying spatiotemporal distributions and mobility patterns. Although these models are often assumed to preserve privacy due to their generative nature, this assumption does not necessarily hold. In this work, we investigate the intersection of generative trajectory modeling and privacy evaluation. By identifying applicable empirical methods for assessing privacy preservation in trajectory generation tasks, we demonstrate a significant gap in the evaluation of privacy for generative trajectory models. Motivated by this gap, we implement Membership Inference Attacks against representative models, demonstrating the feasibility of using such empirical privacy evaluation methods and showing that their generative nature does not eliminate privacy risks.
| Comments: | Accepted at the 1st Workshop on Multi-Sensor Trajectory Knowledge Discovery and Extraction (MuseKDE 2026), co-located with the 27th IEEE International Conference on Mobile Data Management (IEEE MDM 2026) |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.15246 [cs.LG] |
| (or arXiv:2605.15246v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15246 arXiv-issued DOI via DataCite |
From: Stavros Bouras Mr [view email]
[v1]
Thu, 14 May 2026 10:57:34 UTC (104 KB)
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