A new framework evaluates privacy metrics for synthetic tabular data by inserting controlled risks and testing detection under no-box threat models on public datasets.
Truly Anonymous Synthetic Data
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Adversaries can degrade synthetic data quality via small manipulations such as label flipping or feature-importance interventions, substantially harming downstream model performance and increasing statistical divergence from real data.
citing papers explorer
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Empirical Evaluation of Structured Synthetic Data Privacy Metrics: Novel experimental framework
A new framework evaluates privacy metrics for synthetic tabular data by inserting controlled risks and testing detection under no-box threat models on public datasets.
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Quality Degradation Attack in Synthetic Data
Adversaries can degrade synthetic data quality via small manipulations such as label flipping or feature-importance interventions, substantially harming downstream model performance and increasing statistical divergence from real data.