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cs.LG2026
Risk-Equalized Differentially Private Synthetic Data: Protecting Outliers by Controlling Record-Level Influence
Amir Asiaee, Chao Yan, Zachary B. Abrams +1
When synthetic data is released, some individuals are harder to protect than others. A patient with a rare disease combination or a transaction with unusual characteristics stands…
cs.LG2026
PRISM: Differentially Private Synthetic Data with Structure-Aware Budget Allocation for Prediction
Amir Asiaee, Chao Yan, Zachary B. Abrams +1
Differential privacy (DP) provides a mathematical guarantee limiting what an adversary can learn about any individual from released data. However, achieving this protection typical…
cs.LG2026
A Reinforcement Learning Approach to Synthetic Data Generation
Natalia Espinosa-Dice, Nicholas J. Jackson, Chao Yan +2
Synthetic data generation (SDG) is a promising approach for enabling data sharing in biomedical studies while preserving patient privacy. Yet, state-of-the-art generative models of…