Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation

• URL: http://arxiv.org/abs/2412.16083v1
• Date: Fri, 20 Dec 2024 17:30:58 GMT
• Title: Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation
• Authors: Timur Sattarov, Marco Schreyer, Damian Borth,
• Abstract summary: We introduce DP-Fed-FinDiff framework, a novel integration of Differential Privacy, Federated Learning and Denoising Diffusion Probabilistic Models. We demonstrate the effectiveness of DP-Fed-FinDiff on multiple real-world financial datasets. The results affirm the potential of DP-Fed-FinDiff to enable secure data sharing and robust analytics in highly regulated domains.
• Score: 5.182014186927255
• License:
• Abstract: The increasing demand for privacy-preserving data analytics in finance necessitates solutions for synthetic data generation that rigorously uphold privacy standards. We introduce DP-Fed-FinDiff 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 stringent privacy regulations while maintaining data utility. We demonstrate the effectiveness of DP-Fed-FinDiff on multiple real-world financial 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-Fed-FinDiff to enable secure data sharing and robust analytics in highly regulated domains, paving the way for further advances in federated learning and privacy-preserving data synthesis.

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