Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis

TL;DR

This paper presents DP-FedTabDiff, a novel federated diffusion model framework that combines differential privacy to generate high-quality synthetic tabular data while ensuring privacy compliance in sensitive domains.

Contribution

The paper introduces a new federated diffusion model integrating differential privacy for tabular data synthesis, balancing data utility and privacy in a novel way.

Findings

Achieves strong privacy guarantees with high-fidelity data generation.

Demonstrates effective privacy-utility trade-offs across datasets.

Enables secure data sharing in regulated environments.

Abstract

The increasing demand for privacy-preserving data analytics in various domains necessitates solutions for synthetic data generation that rigorously uphold privacy standards. We introduce the DP-FedTabDiff framework, a novel integration of Differential Privacy, Federated Learning and Denoising Diffusion Probabilistic Models designed to generate high-fidelity synthetic tabular data. This framework ensures compliance with privacy regulations while maintaining data utility. We demonstrate the effectiveness of DP-FedTabDiff on multiple real-world mixed-type tabular datasets, achieving significant improvements in privacy guarantees without compromising data quality. Our empirical evaluations reveal the optimal trade-offs between privacy budgets, client configurations, and federated optimization strategies. The results affirm the potential of DP-FedTabDiff to enable secure data sharing and analytics in highly regulated domains, paving the way for further advances in federated learning and privacy-preserving data synthesis.