HT-Fed-GAN: Federated Generative Model for Decentralized Tabular Data Synthesis

In this paper, we study the problem of privacy-preserving data synthesis (PPDS) for tabular data in a distributed multi-party environment. In a decentralized setting, for PPDS, federated generative models with differential privacy are used by the existing methods. Unfortunately, the existing models...

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Detalles Bibliográficos
Autores principales: Duan, Shaoming, Liu, Chuanyi, Han, Peiyi, Jin, Xiaopeng, Zhang, Xinyi, He, Tianyu, Pan, Hezhong, Xiang, Xiayu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858387/
https://www.ncbi.nlm.nih.gov/pubmed/36673229
http://dx.doi.org/10.3390/e25010088

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858387/
https://www.ncbi.nlm.nih.gov/pubmed/36673229
http://dx.doi.org/10.3390/e25010088

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