diffGHOST: Diffusion based Generative Hedged Oblivious Synthetic Trajectories
Florent Guépin, Cheick Tidiani Cisse, Denis Renaud, François Bidet, Arnaud Legendre
TLDR
diffGHOST is a diffusion model that generates privacy-preserving synthetic mobility trajectories by mitigating memorization in a segmented latent space.
Key contributions
- Introduces diffGHOST, a novel conditional diffusion model for synthetic trajectory generation.
- Employs latent space segmentation to enhance privacy protection.
- Identifies and mitigates memorization of critical sensitive samples.
- Aims to provide privacy guarantees while preserving trajectory utility.
Why it matters
Mobility trajectories are sensitive, and existing models often lack privacy guarantees. diffGHOST introduces a conditional diffusion model with latent space segmentation to prevent memorization, ensuring privacy-preserving synthetic data.
Original 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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