Tab-PE extends Private Evolution to tabular data with heuristic operators, outperforming AIM by up to 10% classification accuracy and 28x speed on high-order correlation datasets under differential privacy.
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Synthetic data generation exhibits disparate impact from group-specific approximation, sampling, and estimation errors; group-wise models improve both utility and parity on graphical model methods.
A survey of differential privacy theory, mechanisms, applications, and user-facing issues.
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Differentially Private Synthetic Data via APIs 4: Tabular Data
Tab-PE extends Private Evolution to tabular data with heuristic operators, outperforming AIM by up to 10% classification accuracy and 28x speed on high-order correlation datasets under differential privacy.
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Disparate Impact in Synthetic Data Generation
Synthetic data generation exhibits disparate impact from group-specific approximation, sampling, and estimation errors; group-wise models improve both utility and parity on graphical model methods.