A self-attention-based differentially private tabular GAN with high data utility
Abstract
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.
Cite
@article{arxiv.2312.13031,
title = {A self-attention-based differentially private tabular GAN with high data utility},
author = {Zijian Li and Zhihui Wang},
journal= {arXiv preprint arXiv:2312.13031},
year = {2023}
}