A self-attention-based differentially private tabular GAN with high data utility

Zijian Li; Zhihui Wang

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arXiv · 2312.13031

A self-attention-based differentially private tabular GAN with high data utility

Zijian Li·Zhihui Wang

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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.

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BibTeX RIS

Zijian Li, Zhihui Wang. 2023-12-20. A self-attention-based differentially private tabular GAN with high data utility. https://arxiv.org/abs/2312.13031

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