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.

翻译：金融领域对隐私保护数据分析的需求日益增长，亟需能够严格遵循隐私标准的合成数据生成方案。本文提出DP-Fed-FinDiff框架，该框架创新性地融合了差分隐私、联邦学习与去噪扩散概率模型，旨在生成高保真度的合成表格数据。该框架在确保符合严格隐私法规的同时，保持了数据实用性。我们在多个真实金融数据集上验证了DP-Fed-FinDiff的有效性，在保证数据质量的前提下显著提升了隐私保障水平。实证评估揭示了隐私预算、客户端配置与联邦优化策略之间的最佳权衡关系。研究结果证实了DP-Fed-FinDiff在高度监管领域实现安全数据共享与稳健分析的潜力，为联邦学习与隐私保护数据合成技术的进一步发展开辟了道路。