A Privacy-Preserving Framework for Generative Model-driven Synthetic Datasets

Abstract Despite the advancement of generative model-based synthetic datasets, several challenges, such as privacy attacks and limitations of current privacy-preserving approaches, undermine the trust in this field. This research attempts to alleviate these challenges by developing a novel privacy-preserving framework that will contribute to the practical advancements of synthetic data generation across industry and the public sector.