diffGHOST: A Diffusion Model for Privacy-Preserving Synthetic Trajectories

Researchers have introduced diffGHOST, a novel conditional diffusion model designed to generate synthetic mobility trajectories while rigorously preserving individual privacy. As trajectory data becomes increasingly valuable for various applications, its inherent sensitivity poses significant privacy risks. Existing state-of-the-art generative models often fail to provide robust privacy guarantees, relying on false assumptions about implicit privacy and potentially memorizing critical samples. To address this, diffGHOST employs latent space segmentation to identify and mitigate the memorization of sensitive data points. By leveraging condition segments within a learned latent space, the methodology ensures that generated synthetic trajectories maintain high utility for analysis without compromising personal information. This approach represents a significant advancement in balancing data usability with privacy protection in mobility analytics. The paper, submitted to arXiv under Computer Science > Artificial Intelligence and Cryptography and Security, proposes a technical solution to the growing challenge of data privacy in location-based services, offering a promising tool for researchers and developers seeking to leverage mobility data responsibly.