Table 2.

A summary of the GAN models applied for benchmarking

Model	Distance measure (loss function)	Auto-encoder for discrete data generation	Normalization	Additional privacy components
medGAN25	Jensen-Shannon divergence	Yes	BatchNorm for generator	No
medBGAN35	f-divergence	Yes	BatchNorm for generator	No
EMR-WGAN20	Wasserstein divergence	No	BatchNorm for generator; LayerNorm for discriminator	No
WGAN35	Wasserstein divergence	Yes	BatchNorm for generator	No
DPGAN36	Wasserstein divergence	Yes	BatchNorm for generator	Yes (differentially private stochastic gradient descent)

All models share the generator-discriminator architecture for EHR data synthesis, but differ in their specializations to enhance either utility or privacy.