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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...
| Autores principales: | , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2022
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| Materias: | |
| 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 |