MIDST Challenge at SaTML 2025: Membership Inference over Diffusion-models-based Synthetic Tabular data

ArXi:2603.19185v1 Announce Type: new Synthetic data is often perceived as a silver-bullet solution to data anonymization and privacy-preserving data publishing. Drawn from generative models like diffusion models, synthetic data is expected to preserve the statistical properties of the original dataset while remaining resilient to privacy attacks. Recent developments of diffusion models have been effective on a wide range of data types, but their privacy resilience, particularly for tabular formats, remains largely unexplored.