AAAI 2025technical0 citations
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.
BibTeX
@article{Padariya_2025, title={A Privacy-Preserving Framework for Generative Model-driven Synthetic Datasets}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35222}, DOI={10.1609/aaai.v39i28.35222}, abstractNote={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.}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Padariya, Debalina R}, year={2025}, month={Apr.}, pages={29289-29290} }