arXiv
Open Access
2023
FFPDG: Fast, Fair and Private Data Generation
Weijie Xu
Jinjin Zhao
Francis Iannacci
Bo Wang
Abstrak
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.
Penulis (4)
W
Weijie Xu
J
Jinjin Zhao
F
Francis Iannacci
B
Bo Wang
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2023
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓