用差分隐私与联邦学习保护心脏病数据，预测准确率达85%。
Differential Privacy-Driven Framework for Enhancing Heart Disease Prediction
- 结合差分隐私加噪与联邦学习，在不集中数据前提下训练模型。
- 在心脏病数据上实现85%测试准确率，兼顾隐私与性能。
- 适合关注医疗数据安全的医疗机构与研究人员。
随着医疗系统数字化进程加快，个人健康数据的生成与共享激增。保护患者隐私对于维持公众信任和遵守数据保护法规至关重要。机器学习在医疗领域发挥关键作用，支持个性化治疗、早期疾病检测、预测分析、图像解读、药物发现、运营优化及患者监测，提升决策质量，加速研究进程，减少错误并改善患者预后。本文采用机器学习方法，包括差分隐私与联邦学习，构建隐私保护模型，使医疗相关方能在不泄露个体隐私的前提下获取洞察。差分隐私通过向数据添加噪声保障统计隐私，联邦学习则实现跨分散数据集的协同模型训练。我们探索将这些技术应用于心脏病数据，证明其在确保隐私的同时提供有效分析。结果表明，结合差分隐私的联邦学习模型达到85%的测试准确率，全程保障患者数据的安全与私密性。
原文摘要 · Abstract (English)
With the rapid digitalization of healthcare systems, there has been a substantial increase in the generation and sharing of private health data. Safeguarding patient information is essential for maintaining consumer trust and ensuring compliance with legal data protection regulations. Machine learning is critical in healthcare, supporting personalized treatment, early disease detection, predictive analytics, image interpretation, drug discovery, efficient operations, and patient monitoring. It enhances decision-making, accelerates research, reduces errors, and improves patient outcomes. In this paper, we utilize machine learning methodologies, including differential privacy and federated learning, to develop privacy-preserving models that enable healthcare stakeholders to extract insights without compromising individual privacy. Differential privacy introduces noise to data to guarantee statistical privacy, while federated learning enables collaborative model training across decentralized datasets. We explore applying these technologies to Heart Disease Data, demonstrating how they preserve privacy while delivering valuable insights and comprehensive analysis. Our results show that using a federated learning model with differential privacy achieved a test accuracy of 85%, ensuring patient data remained secure and private throughout the process.
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