TabPATE applies a PATE-style private aggregation to synthetic tabular queries generated from feature ranges, enabling private in-context learning with near-random membership inference success while keeping competitive utility.
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Pith papers citing it
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cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
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.
PersonaLedger LLM simulator achieves AUC 0.70 for fraud detection at epsilon=1 from DP inputs but shows significant distribution drift due to learned priors overriding input statistics on temporal and demographic features.
citing papers explorer
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TabPATE: Differentially Private Tabular In-Context Learning Without Public Data
TabPATE applies a PATE-style private aggregation to synthetic tabular queries generated from feature ranges, enabling private in-context learning with near-random membership inference success while keeping competitive utility.
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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.
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Evaluating LLM Simulators as Differentially Private Data Generators
PersonaLedger LLM simulator achieves AUC 0.70 for fraud detection at epsilon=1 from DP inputs but shows significant distribution drift due to learned priors overriding input statistics on temporal and demographic features.