High-quality data are essential for machine learning and data-driven research, yet data scarcity and privacy concerns remain major obstacles in many domains. Generative models have recently emerged as a promising approach to synthesize data that follow the same statistical distribution as real datasets. However, generative models are vulnerable to membership inference attacks, which threaten data confidentiality by exploiting model outputs to infer whether specific samples were used in training. Existing defense strategies struggle to simultaneously preserve data utility and provide robust privacy protection. To address this challenge, we propose our PPGM-GAN, a Privacy-Preserving GAN for synthetic data against membership inference attack to balance both data utility and data privacy. PPGM-GAN balances privacy and utility through a privacy-utility tradeoff function that quantifies and optimizes both aspects under different adversarial knowledge. To enhance data utility, we incorporate conditional generation and key-attribute screening to ensure sufficient representation of infrequent attribute values. Additionally, differential privacy is employed during training to prevent overfitting and reduce privacy leakage. Experimental results demonstrate that PPGM-GAN outperforms state-of-the-art privacy-preserving generative models, producing high-utility synthetic data under the same privacy constraints.
Cui et al. (Fri,) studied this question.