Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective

Training generative machine learning models to produce synthetic tabular data has become a popular approach for enhancing privacy in data sharing. As this typically involves processing sensitive personal information, releasing either the trained model or generated synthetic datasets can still pose p