AI 中文总结
研究探索同态加密和差分隐私技术整合用于联邦学习系统，以增强数据隐私与安全，经数据集测试，既能增强隐私又不显著损害模型准确性，还分析了数据异质性影响，得出相关策略可提高效率，能用于敏感领域。
AI 中文摘要
本研究探索同态加密和差分隐私技术的整合，以增强联邦学习（FL）系统中的数据隐私与安全。FL允许数据保留在本地设备，无需集中收集数据，但模型更新期间敏感信息仍可能泄露。同态加密实现对加密数据的计算，差分隐私通过应用于模型输出的统计技术防止提取个体信息。所提架构在弗雷明汉、皮马印第安人糖尿病和银行营销数据集上测试，结果显示增强隐私同时不会显著损害模型准确性。此外，分析了客户端数据异质性对模型性能的影响，得出精心选择差分隐私参数和训练设置以及使用更大数据集等策略可提高FL效率。研究结果表明，可在医疗和金融等敏感领域安全应用隐私保护且高性能的人工智能系统。
英文摘要
This study explores the integration of homomorphic encryption and differential privacy techniques to enhance data privacy and security in Federated Learning (FL) systems. FL allows data to remain on local devices, eliminating the need for centralized data collection; however, sensitive information may still be leaked during model updates. To address this issue, homomorphic encryption enables computations on encrypted data, while differential privacy prevents the extraction of individual information through statistical techniques applied to model outputs. The proposed architecture was tested on the Framingham, Pima Indians Diabetes, and Bank Marketing datasets, revealing that enhanced privacy can be achieved without significantly compromising model accuracy. Furthermore, the impact of data heterogeneity among clients on model performance was analyzed, and it was concluded that strategies such as the careful selection of differential privacy parameters and training settings, along with the use of larger datasets, can improve the efficiency of FL. The findings demonstrate that privacy-preserving and high-performance artificial intelligence systems can be securely applied in sensitive domains such as healthcare and finance.
