SiloFuse: Cross-silo Synthetic Data Generation with Latent Tabular Diffusion Models
This addresses the need for enterprises with proprietary data to share and augment data across silos without compromising privacy, though it is incremental as it builds on diffusion models for a specific domain.
The paper tackles the problem of generating synthetic tabular data from features distributed across multiple silos, which existing synthesizers struggle with due to privacy and communication constraints, and introduces SiloFuse, a distributed latent tabular diffusion framework that achieves 43.8 and 29.8 higher percentage points over GANs in resemblance and utility while ensuring privacy and communication efficiency.
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 for each client's features, masking their actual values. We employ stacked distributed training to improve communication efficiency, reducing the number of rounds to a single step. Under SiloFuse, we prove the impossibility of data reconstruction for vertically partitioned synthesis and quantify privacy risks through three attacks using our benchmark framework. Experimental results on nine datasets showcase SiloFuse's competence against centralized diffusion-based synthesizers. Notably, SiloFuse achieves 43.8 and 29.8 higher percentage points over GANs in resemblance and utility. Experiments on communication show stacked training's fixed cost compared to the growing costs of end-to-end training as the number of training iterations increases. Additionally, SiloFuse proves robust to feature permutations and varying numbers of clients.