Healthcare has become exceptionally sophisticated, as wearables and connected medical devices are revolutionising 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 extremely important in data-driven healthcare. In this paper, we propose a multi-layer IoT, Edge and Cloud architecture to enhance the speed of response for emergency healthcare by distributing tasks based on response criticality and permanence of storage. Privacy of patient data is assured by proposing a Differential Privacy framework across several machine learning models such as 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 86% 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 82-84% accuracy while reducing attribute inference attacks by up to 18% and data reconstruction correlation by 70%. Blockchain security further 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：随着可穿戴设备和联网医疗设备正在彻底改变远程患者监测、应急响应、药物管理、诊断以及预测性和规范性分析，医疗保健已变得异常复杂。物联网与云计算集成系统（IoT-Cloud）为这些医疗应用提供了感知、自动化和处理支持。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也极为重要。本文提出一种多层物联网、边缘与云架构，通过基于响应关键性和存储持久性来分配任务，以提升紧急医疗的响应速度。通过为K-means、逻辑回归、随机森林和朴素贝叶斯等多种机器学习模型提出一个差分隐私框架，确保了患者数据的隐私。我们建立了一个全面的威胁模型，识别了三类攻击者，并在不同隐私预算下评估了拉普拉斯、高斯及混合噪声机制，其中监督学习算法达到了高达86%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方法，为低维和高维数据集均提供了适中的尾部特性以及更优的隐私-效用权衡。在$\varepsilon = 5.0$的实际阈值下，监督学习算法实现了82-84%的准确率，同时将属性推断攻击降低了高达18%，并将数据重建相关性降低了70%。区块链安全通过时间戳、可追溯性和不可篡改性进一步确保了分析应用中的可信通信。边缘计算在紧急场景下实现了8$\times$的延迟降低，验证了该分层架构对时间关键型操作的有效性。