Paper: arXiv 2412.16083

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.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"]

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1 of 314 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.