Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis

ArXiv ID: 2412.16083 “View on arXiv”

Authors: Unknown

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.

Keywords: Differential Privacy, Federated Learning, Diffusion Models, Synthetic Data, Data Privacy, Multi-Asset

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts including differential privacy theory and diffusion model derivations, while also presenting extensive empirical evaluations on real-world tabular datasets to validate the framework’s efficacy and trade-offs.

  flowchart TD
    A["Research Goal: Develop DP-FedTabDiff<br>for privacy-preserving tabular data synthesis"] --> B["Methodology: Federated Diffusion<br>with Differential Privacy"]
    B --> C{"Input: Mixed-Type Tabular<br>Datasets from Multiple Clients"}
    C --> D["Computations: Federated Training of<br>Denoising Diffusion Probabilistic Models"]
    D --> E["Mechanism: Apply Differential Privacy<br>to Gradient Updates"]
    E --> F{"Outcomes: Privacy Guarantees<br>vs. Synthetic Data Utility"}
    F --> G["Findings: Optimal Trade-offs Achieved<br>Enabling Secure Data Sharing"]