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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.15246 |
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| _version_ | 1866911686058835968 |
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| author | Bouras, Stavros Kontopoulos, Ioannis Pugliese, Chiara Lettich, Francesco Carlini, Emanuele Kavalionak, Hanna Renso, Chiara Tserpes, Konstantinos |
| author_facet | Bouras, Stavros Kontopoulos, Ioannis Pugliese, Chiara Lettich, Francesco Carlini, Emanuele Kavalionak, Hanna Renso, Chiara Tserpes, Konstantinos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15246 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Privacy Evaluation of Generative Models for Trajectory Generation Bouras, Stavros Kontopoulos, Ioannis Pugliese, Chiara Lettich, Francesco Carlini, Emanuele Kavalionak, Hanna Renso, Chiara Tserpes, Konstantinos Machine Learning 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. |
| title | Privacy Evaluation of Generative Models for Trajectory Generation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.15246 |