Generative modeling has been used frequently in synthetic data generation. Fairness and privacy are two big concerns for synthetic data. Although Recent GAN [\cite{goodfellow2014generative}] based methods show good results in preserving privacy, the generated data may be more biased. At the same time, these methods require high computation resources. In this work, we design a fast, fair, flexible and private data generation method. We show the effectiveness of our method theoretically and empirically. We show that models trained on data generated by the proposed method can perform well (in inference stage) on real application scenarios.
@article{arxiv.2307.00161,
title = {FFPDG: Fast, Fair and Private Data Generation},
author = {Weijie Xu and Jinjin Zhao and Francis Iannacci and Bo Wang},
journal= {arXiv preprint arXiv:2307.00161},
year = {2023}
}
Comments
12 pages, 2 figures, ICLR 2021 Workshop on Synthetic Data Generation