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.

翻译：生成对抗网络（GANs）已成为数据生成中的通用技术，其在图像生成方面的能力已得到充分验证。然而，将其应用于表格数据生成的效果并不理想。此外，尝试将差分隐私技术融入这些框架往往会导致数据效用的下降。为应对这些挑战，本文提出了一种名为DP-SACTGAN的新型条件生成对抗网络（CGAN）框架，用于差分隐私表格数据生成，旨在克服上述障碍。实验结果表明，DP-SACTGAN不仅能够准确建模原始数据的分布，还能有效满足差分隐私的要求。