用扩散模型生成隐私保护的轨迹数据，防止敏感信息泄露。
diffGHOST: Diffusion based Generative Hedged Oblivious Synthetic Trajectories
- 基于潜在空间分段的条件扩散模型，避免关键样本记忆
- 生成轨迹保留真实模式，同时提供可证明的隐私保障
- 适合需要高隐私保护的交通、定位类研究应用
轨迹数据在众多应用中具有重要价值，但其本身高度敏感，包含个人行踪等隐私信息。为应对这一挑战，生成合成轨迹成为兼顾数据利用与隐私保护的可行方案。现有先进模型常依赖生成模型隐式隐私的错误假设，无法提供切实的隐私保障，且难以兼顾轨迹实用性。本文提出 diffGHOST，一种基于潜在空间分段的条件扩散模型，通过识别并缓解关键样本的记忆现象，实现对敏感轨迹数据的有效匿名化。该方法在保持轨迹时空模式真实性的同时，提供可验证的隐私保护能力，适用于需高隐私保障的移动行为分析场景。
原文摘要 · Abstract (English)
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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