Eastern University

Synthetic electronic health records have long traded clinical fidelity against formal privacy guarantees. SynthEHR-DP combines a mixed-type encoder, a temporal transformer encoder and a conditional variational autoencoder, trained under differentially private stochastic gradient descent with Rényi privacy accounting, then applies a post-hoc clinical constraint layer that spends no privacy budget. On MIMIC-IV at a total budget of ε=8 and δ=10⁻⁵, membership inference AUC is 0.52, downstream TSTR AUROC is 0.786, 4.2% below the non-private TRTR oracle of 0.821, and the clinical validity score is 0.961.

Synthetic electronic health records have long traded clinical fidelity against formal privacy guarantees. SynthEHR-DP combines a mixed-type encoder, a temporal transformer encoder and a conditional variational autoencoder, trained under differentially private stochastic gradient descent with Rényi privacy accounting, then applies a post-hoc clinical constraint layer that spends no privacy budget. On MIMIC-IV at a total budget of ε=8 and δ=10⁻⁵, membership inference AUC is 0.52, downstream TSTR AUROC is 0.786, 4.2% below the non-private TRTR oracle of 0.821, and the clinical validity score is 0.961.