PrAda-GAN: A Private Adaptive Generative Adversarial Network with Bayes Network Structure

The introduction of PrAda-GAN marks a significant advancement in the realm of synthetic data generation under differential privacy. By integrating the strengths of GAN-based and marginal-based approaches, this method employs a sequential generator architecture that adeptly captures complex dependencies among variables. The theoretical framework established in the study demonstrates diminishing bounds on parameter distance, variable selection error, and Wasserstein distance, indicating a robust foundation for its efficacy. Empirical results further validate the method's superiority, showcasing its performance over existing tabular data synthesis techniques in terms of the privacy-utility trade-off. This development is particularly relevant as the demand for privacy-preserving data synthesis continues to grow, positioning PrAda-GAN as a critical tool for researchers and practitioners in artificial intelligence and data science.