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

• URL: http://arxiv.org/abs/2312.13031v1
• Date: Wed, 20 Dec 2023 13:55:56 GMT
• Title: A self-attention-based differentially private tabular GAN with high data utility
• Authors: Zijian Li, Zhihui Wang
• Abstract summary: This paper introduces DP-SACTGAN, a Conditional Generative Adversarial Network (CGAN) framework for differentially private data generation. Experimental findings demonstrate that DP-SACTGAN accurately models the distribution of the original data and effectively satisfies the requirements of differential privacy.
• Score: 23.99149917513586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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