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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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
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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.