FFPDG: Fast, Fair and Private Data Generation

Generative modeling has been used frequently in synthetic data generation. Fairness and privacy are two big concerns for synthetic data. Although Recent GAN [Goodfellow et al. (2014)] 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. 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. Synthetic data [Rubin (1993)] is data that is artificially created rather than being generated by actual events.