diffGHOST: Diffusion based Generative Hedged Oblivious Synthetic Trajectories
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914554203602944 |
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| author | Guépin, Florent Cisse, Cheick Tidiani Renaud, Denis Bidet, François Legendre, Arnaud |
| author_facet | Guépin, Florent Cisse, Cheick Tidiani Renaud, Denis Bidet, François Legendre, Arnaud |
| contents | Trajectories are nowadays valuable information for a wide range of applications. However they are also inherently sensitive, as they contain highly personal information about individuals. Facing this challenge, synthesizing mobility trajectories has emerged as a promising solution to leverage mobility information while preserving privacy. State-of-the-art models, often rely on the false assumptions of generative models implicit privacy and fails to provide privacy guarantees while preserving trajectories utility. Here, we introduce diffGHOST, a conditional diffusion model based on latent space segmentation, designed to answer this challenge. Thus, this paper propose a methodology that identify and mitigate memorization of critical samples using condition segments of a learn latent space. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10647 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | diffGHOST: Diffusion based Generative Hedged Oblivious Synthetic Trajectories Guépin, Florent Cisse, Cheick Tidiani Renaud, Denis Bidet, François Legendre, Arnaud Artificial Intelligence Cryptography and Security Trajectories are nowadays valuable information for a wide range of applications. However they are also inherently sensitive, as they contain highly personal information about individuals. Facing this challenge, synthesizing mobility trajectories has emerged as a promising solution to leverage mobility information while preserving privacy. State-of-the-art models, often rely on the false assumptions of generative models implicit privacy and fails to provide privacy guarantees while preserving trajectories utility. Here, we introduce diffGHOST, a conditional diffusion model based on latent space segmentation, designed to answer this challenge. Thus, this paper propose a methodology that identify and mitigate memorization of critical samples using condition segments of a learn latent space. |
| title | diffGHOST: Diffusion based Generative Hedged Oblivious Synthetic Trajectories |
| topic | Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2605.10647 |