SiloFuse: Cross-silo Synthetic Data Generation with Latent Tabular Diffusion Models

arXiv:2404.03299v1 Announce Type: cross
Abstract: Synthetic tabular data is crucial for sharing and augmenting data across silos, especially for enterprises with proprietary data. However, existing synthesizers are designed for centrally stored data. Hence, they struggle with real-world scenarios where features are distributed across multiple silos, necessitating on-premise data storage. We introduce SiloFuse, a novel generative framework for high-quality synthesis from cross-silo tabular data. To ensure privacy, SiloFuse utilizes a distributed latent tabular diffusion architecture. Through autoencoders, latent representations are learned …

arxiv cs.cr cs.db cs.dc cs.lg data data storage diffusion models distributed enterprises features premise proprietary data real sharing silos storage synthetic synthetic data world