In recent years, the financial sector has faced growing pressure to adopt advanced machine learning models to derive valuable insights while preserving data privacy. However, the highly sensitive nature of financial data presents significant challenges to sharing and collaboration. This paper presents DPFedBank, an innovative framework enabling financial institutions to collaboratively develop machine learning models while ensuring robust data privacy through Local Differential Privacy (LDP) mechanisms. DPFedBank is designed to address the unique privacy and security challenges associated with financial data, allowing institutions to share insights without exposing sensitive information. By leveraging LDP, the framework ensures that data remains confidential even during collaborative processes, providing a crucial solution for privacy-aware machine learning in finance. We conducted an in-depth evaluation of the potential vulnerabilities within this framework and developed a comprehensive set of policies aimed at mitigating these risks. The proposed policies effectively address threats posed by malicious clients, compromised servers, inherent weaknesses in existing Differential Privacy-Federated Learning (DP-FL) frameworks, and sophisticated external adversaries. Unlike existing DP-FL approaches, DPFedBank introduces a novel combination of adaptive LDP mechanisms and advanced cryptographic techniques specifically tailored for financial data, which significantly enhances privacy while maintaining model utility. Key security enhancements include the implementation of advanced authentication protocols, encryption techniques for secure data exchange, and continuous monitoring systems to detect and respond to malicious activities in real-time.

翻译：近年来，金融行业面临着日益增长的压力，需要在采用先进机器学习模型获取价值洞察的同时确保数据隐私。然而，金融数据的高度敏感性给数据共享与协作带来了重大挑战。本文提出DPFedBank，这是一个创新性框架，使金融机构能够通过本地差分隐私机制在协作开发机器学习模型的同时确保强大的数据隐私保护。DPFedBank旨在应对金融数据特有的隐私与安全挑战，使机构能够在共享洞察时不暴露敏感信息。通过利用LDP机制，该框架确保即使在协作过程中数据仍保持机密性，为金融领域隐私感知的机器学习提供了关键解决方案。我们对该框架内的潜在漏洞进行了深入评估，并制定了一套全面的政策以缓解这些风险。所提出的政策有效应对了恶意客户端、受攻击服务器、现有差分隐私-联邦学习框架固有缺陷以及复杂外部对手构成的威胁。与现有DP-FL方法不同，DPFedBank创新性地结合了自适应LDP机制与专为金融数据设计的高级密码学技术，在保持模型效用的同时显著增强了隐私保护。关键安全增强措施包括实施高级身份验证协议、用于安全数据交换的加密技术，以及实时检测和响应恶意活动的持续监控系统。