FFPDG: Fast, Fair and Private Data Generation
Weijie Xu Jinjin Zhao Francis Iannacci Bo Wang
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.