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

Padariya, Debalina

doi:10.1609/AAAI.V39I28.35222

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

Debalina Padariya

AAAI 2025 pp. 29289-29290

doi:10.1609/AAAI.V39I28.35222 /aaai/2025/padariya2025aaai-privacy/

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.

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Cite

Text

Padariya. "A Privacy-Preserving Framework for Generative Model-Driven Synthetic Datasets." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35222

Markdown

[Padariya. "A Privacy-Preserving Framework for Generative Model-Driven Synthetic Datasets." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/padariya2025aaai-privacy/) doi:10.1609/AAAI.V39I28.35222

BibTeX

@inproceedings{padariya2025aaai-privacy,
  title     = {{A Privacy-Preserving Framework for Generative Model-Driven Synthetic Datasets}},
  author    = {Padariya, Debalina},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {29289-29290},
  doi       = {10.1609/AAAI.V39I28.35222},
  url       = {https://mlanthology.org/aaai/2025/padariya2025aaai-privacy/}
}