חשוב מחדש טענות לאנונימיות בדיווח נתונים סינתטיים: מבט מודל-מרכזי על פגיעה בפרטיות

תקציר מקורי באנגליתarXiv:2601.22434v2 Announce Type: replace-cross Abstract: 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 privacy risks. Yet, recent research, commercial deployments, and privacy regulations like the General Data Protection Regulation (GDPR) largely assess anonymity at the level of an individual dataset. In this paper, we rethink anonymity claims about synthetic data from a model-centric perspective, arguing that meaningful assessments must account for the underlying generative model and be grounded in state-of-the-art privacy attacks. This pe