Abstract: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.
| Subjects: | Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR) |
| Cite as: | arXiv:2605.10647 [cs.AI] |
| (or arXiv:2605.10647v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.10647 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Florent Guépin [view email]
[v1]
Mon, 11 May 2026 14:33:47 UTC (35,460 KB)