Healthcare has become exceptionally sophisticated, as wearables and connected medical devices revolutionize remote patient monitoring, emergency response, medication management, diagnosis, and predictive and prescriptive analytics. Internet of Things and Cloud computing integrated systems (IoT-Cloud) facilitate sensing, automation, and processing for these healthcare applications. While real-time response is crucial for alleviating patient emergencies, protecting patient privacy is paramount in data-driven healthcare. In this paper, we propose a multi-layer IoT, Edge, and Cloud architecture to enhance emergency healthcare response times by distributing tasks based on response criticality and data permanence requirements. We ensure patient privacy through a Differential Privacy framework applied across several machine learning models: K-means, Logistic Regression, Random Forest, and Naive Bayes. We establish a comprehensive threat model identifying three adversary classes and evaluate Laplace, Gaussian, and hybrid noise mechanisms across varying privacy budgets, with supervised algorithms achieving up to 83.6% accuracy. The proposed hybrid Laplace-Gaussian noise mechanism with adaptive budget allocation provides a balanced approach, offering moderate tails and better privacy-utility trade-offs for both low and high-dimension datasets. At the practical threshold of $\varepsilon$=5.0, supervised algorithms achieve 80-81% accuracy while reducing attribute inference attacks by up to 18% and data reconstruction correlation by 70%. We further enhance security through Blockchain integration, which ensures trusted communication through time-stamping, traceability, and immutability for analytics applications. Edge computing demonstrates 8$\times$ latency reduction for emergency scenarios, validating the hierarchical architecture for time-critical operations.

翻译：医疗保健已变得异常复杂，可穿戴设备和互联医疗设备彻底改变了远程患者监测、应急响应、药物管理、诊断以及预测性和规范性分析。物联网与云计算集成系统（IoT-Cloud）为这些医疗应用促进了感知、自动化和处理。虽然实时响应对于缓解患者紧急情况至关重要，但在数据驱动的医疗保健中，保护患者隐私是首要任务。本文提出了一种多层物联网、边缘和云架构，通过根据响应的关键性和数据持久性要求分配任务，来缩短紧急医疗响应时间。我们通过一种跨多个机器学习模型（K-均值、逻辑回归、随机森林和朴素贝叶斯）应用的差分隐私框架来确保患者隐私。我们建立了一个全面的威胁模型，识别了三类对手，并评估了在不同隐私预算下，拉普拉斯、高斯和混合噪声机制的性能，其中监督算法达到了高达83.6%的准确率。所提出的具有自适应预算分配的混合拉普拉斯-高斯噪声机制提供了一种平衡的方法，对于低维和高维数据集都表现出适度的尾部和更好的隐私-效用权衡。在实际阈值$\varepsilon$=5.0下，监督算法达到80-81%的准确率，同时将属性推断攻击降低了高达18%的数据重建相关性降低了70%。我们通过区块链集成进一步增强了安全性，该集成通过时间戳、可追溯性和不变性确保了分析应用的可信通信。边缘计算在紧急场景下展示了8倍的延迟降低，验证了该分层架构在时间关键型操作中的有效性。