High-Quality Synthetic Data Generation for Cross-Silo Tabular Data with Latent Diffusion Models

Core Concepts

SiloFuse, a novel distributed framework for training latent tabular diffusion models, generates high-quality synthetic data for cross-silo tabular datasets while preserving privacy.

Abstract

SiloFuse is a novel distributed framework for training latent tabular diffusion models on cross-silo data. It has the following key features:

Latent Tabular Diffusion Model:

Combines autoencoders and latent diffusion models to unify discrete and continuous tabular features into a shared continuous latent space.
The latent diffusion model captures feature correlations across silos by centralizing the latents.

Stacked Training Paradigm:

Trains local autoencoders at the clients in parallel, followed by latent diffusion model training at the coordinator/server.
Decoupling the training of the two components lowers communication to a single round, overcoming the high costs of end-to-end training.

Benchmarking Framework:

Computes a resemblance score by combining five statistical measures and a utility score by comparing downstream task performance.
Proves the impossibility of data reconstruction when the synthetic data is kept vertically partitioned and quantifies the privacy risks of centralizing synthetic data using three attacks.

Experimental results on nine datasets show that SiloFuse is competitive against centralized methods while efficiently scaling with the number of training iterations.

Stats

SiloFuse achieves 43.8 and 29.8 higher percentage points over GANs in resemblance and utility, respectively.
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.

Quotes

"SiloFuse is a novel distributed framework for training latent tabular diffusion models on cross-silo data."
"SiloFuse generates high-quality synthetic data for cross-silo tabular datasets while preserving privacy."

Key Insights Distilled From

SiloFuse

by Aditya Shank... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03299.pdf

Deeper Inquiries

How can SiloFuse's approach be extended to other types of distributed data beyond tabular data, such as images or time series

SiloFuse's approach can be extended to other types of distributed data beyond tabular data, such as images or time series, by adapting the underlying principles of latent space representation and distributed training.
For images, a similar approach could involve using convolutional neural networks (CNNs) to encode the images into a latent space representation. Each client could have its own CNN encoder to convert the image data into latent features, which are then centralized and used by a generative model for synthesis. The decoders at each client can then reconstruct the synthetic images from the shared latent space.
When dealing with time series data, recurrent neural networks (RNNs) or transformers can be used to encode the temporal information into latent representations. The process would involve encoding the time series data at each client, centralizing the latent representations, and then generating synthetic time series data using a generative model.
By adapting the architecture and training process to suit the specific characteristics of image or time series data, SiloFuse's approach can be extended to a variety of distributed data types beyond tabular data.

What are the potential limitations or drawbacks of the latent space approach used in SiloFuse, and how could they be addressed

While the latent space approach used in SiloFuse offers several advantages, there are potential limitations and drawbacks that need to be considered:

Dimensionality Reduction Challenges: One limitation is the potential loss of information during the encoding process, especially when dealing with high-dimensional data. This can lead to information loss and reduced fidelity in the generated synthetic data. To address this, techniques like variational autoencoders (VAEs) or more complex latent space models can be explored to capture more nuanced relationships in the data.

Interpretability Concerns: Another drawback is the interpretability of the latent space representations. Understanding the meaning of each dimension in the latent space can be challenging, making it harder to interpret the generated data. Techniques like disentangled representation learning can help in disentangling different factors of variation in the latent space.

Generalization Issues: The latent space approach may struggle with generalizing to unseen data distributions or outliers. Regularization techniques and data augmentation methods can be employed to improve the generalization capabilities of the model.

To address these limitations, further research can focus on exploring advanced techniques in latent space modeling, regularization methods, and interpretability tools to enhance the performance and robustness of the latent space approach in SiloFuse.

How might the privacy guarantees of SiloFuse's vertically partitioned synthesis be further strengthened or extended to other privacy-preserving data sharing scenarios

To further strengthen the privacy guarantees of SiloFuse's vertically partitioned synthesis and extend them to other privacy-preserving data sharing scenarios, the following strategies can be considered:

Differential Privacy: Implementing differential privacy mechanisms can add an extra layer of privacy protection by ensuring that the presence or absence of a single individual's data does not significantly impact the overall outcome of the synthesis process. This can help in mitigating the risks associated with singling out attacks.

Secure Multi-Party Computation: Utilizing secure multi-party computation protocols can enable data sharing and synthesis without revealing the raw data to any party involved. This cryptographic approach ensures that computations are performed on encrypted data, preserving privacy while allowing collaborative analysis.

Homomorphic Encryption: Employing homomorphic encryption techniques can enable computations on encrypted data without the need to decrypt it, thereby maintaining data privacy throughout the synthesis process. This can prevent attribute inference attacks by keeping the data encrypted at all times.

Federated Learning: Extending the federated learning approach to SiloFuse can allow for collaborative model training across distributed data sources without sharing raw data. This decentralized training method ensures privacy by keeping the data local to each party while still enabling model improvement through shared learning.

By incorporating these advanced privacy-preserving techniques and protocols, SiloFuse's privacy guarantees can be further strengthened, and the framework can be adapted to a wider range of privacy-sensitive data sharing scenarios beyond vertically partitioned synthesis.