A self-attention-based differentially private tabular GAN with high data utility
This addresses the challenge of privacy-preserving data generation for tabular datasets, which is incremental as it builds on existing GAN and differential privacy methods.
The paper tackled the problem of generating high-utility tabular data with differential privacy by introducing DP-SACTGAN, a novel CGAN framework, and demonstrated that it accurately models data distribution while meeting privacy requirements.
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.