PK-Free, Blind and Collusion-Resistant Synthetic Tabular Fingerprinting With Diffusion Models

This research paper addresses security risks from synthetic tabular data (AI-generated fake datasets) by proposing PBC-TabFip, a fingerprinting framework that embeds hidden identifiers into synthetic data to detect unauthorized copying and identify who leaked it. The framework uses diffusion models (AI systems that generate data by gradually refining random noise) and Tardos codes (a mathematical scheme for tracking which user leaked protected content) to protect synthetic tables even when primary keys (unique identifiers for database rows) are missing or altered, and to resist collusion attacks (when multiple users combine their copies to remove the fingerprint).