causal framework

New Auditing Framework Detects Synthetic Data Privacy Leaks Without Model Access

A new causal framework for auditing synthetic data detects privacy leaks by distinguishing true disclosures from phantom ones. It uses statistical hypothesis testing with holdout sets, requires no model access or canary insertion, and is orders of magnitude more efficient than shadow-model approaches.