A self-attention-based differentially private tabular GAN with high data
  utility
Zijian Li Zhihui Wang
Generative Adversarial Networks (GANs) have become a ubiquitous technology
for data generation, with their prowess in image generation being
well-established. However, their application in generating tabular data has
been less than ideal. Furthermore, attempting to incorporate differential
privacy technology into these frameworks has often resulted in a degradation of
data utility. To tackle these challenges, this paper introduces DP-SACTGAN, a
novel Conditional Generative Adversarial Network (CGAN) framework for
differentially private tabular data generation, aiming to surmount these
obstacles. Experimental findings demonstrate that DP-SACTGAN not only
accurately models the distribution of the original data but also effectively
satisfies the requirements of differential privacy.