diffGHOST: Diffusion based Generative Hedged Oblivious Synthetic Trajectories

• URL: http://arxiv.org/abs/2605.10647v1
• Date: Mon, 11 May 2026 14:33:47 GMT
• Title: diffGHOST: Diffusion based Generative Hedged Oblivious Synthetic Trajectories
• Abstract summary: We introduce diffGHOST, a conditional diffusion model based on latent space segmentation.<n>This paper propose a methodology that identify and memorization of critical samples using condition segments of a learn latent space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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