Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective
The paper argues that claims of synthetic data anonymity are flawed because they ignore how generative models actually work. It maps regulatory risks like singling out, linkability, and inferences to specific privacy attacks. It concludes that Differential Privacy (DP) is the only method capable of robustly mitigating these risks against model-centric threats.