CICI: Secure Data Architecture: STREAMS: Secure Transport and REsearch Architecture for Monitoring Stroke Recovery

脑卒中患者的康复是一个长期而关键的过程。通过可穿戴传感器和网络摄像头监测患者，可以减少意外跌倒和饮食摄入不当等事件的风险，从而支持在家康复。这些由传感器生成的关于患者状态和活动的实时数据流在数据中心进行处理，以便进行实时分析，从而帮助医疗保健专业人员快速有效地响应患者的需求。由于数据流可能包含电子受保护的健康信息（ePHI），因此必须对其进行保护，以便传输和使用符合安全和隐私法规，例如《健康保险流通与责任法案》（HIPAA）和适用的州法律。因此，研究先进的网络和计算技术来满足这些安全要求是很重要的，这对于将传感器和数据分析从研究带到临床环境至关重要。本研究计划通过开发一种用于监测中风恢复的安全传输和研究架构（STREAMS）来解决传输和处理患者相关传感器数据的安全挑战，这是一种技术概念验证实现，使用安全的软件定义网络和弹性计算和存储资源来保护端到端传感器数据流。STREAMS将成为安全网络架构的第一个原型，在现实的临床环境中为中风患者提供先进的基于数据分析的医疗保健。该项目解决了保护异构感官epi患者数据的问题。它捕获患者数据分析的工作流，并定义一个基于角色的安全实施框架来应用访问策略。安全SDN控制器将被设计用于验证、识别和引导加密数据流，以确保网络上的数据流符合HIPAA标准，为云计算资源的配置提供指导，并根据用户的角色、优先级、传感器数据流的类型和来源以及网络条件应用最合适的解密算法。一个通用的安全硬件和软件架构收集、加密、解密、存储、传输、分析和维护来自这些多模态传感器的数据的完整性和可用性，使它们能够使用分析算法进行融合，以了解与中风恢复高度相关的患者活动。高度跨学科的项目团队由医疗保健专业人员、医学研究人员、计算机科学家、IT人员、工程人员和工业合作伙伴组成。

The rehabilitation of stroke patients is a long but critical process for their long-term wellness. Monitoring patients with wearable sensors and web cameras can support at-home rehabilitation by reducing the risk of events such as accidental falls and inappropriate dietary intake. Such sensor-generated live data streams about patient status and activities are processed at data centers for real-time analytics, helping healthcare professionals to respond to patients' needs quickly and effectively. Since the data streams may contain electronic Protected Health Information (ePHI), they must be protected so that transmission and usage conform to security and privacy regulations, such as Health Insurance Portability and Accountability Act (HIPAA) and applicable state laws. Therefore, it is important to investigate advanced networking and computing technologies to meet these security requirements, which are critical for bringing sensors and data analytics from research to clinical environments.This research plans to address the security challenges in transferring and processing patient related sensor data by developing a Secure Transport and REsearch Architecture for Monitoring Stroke Recovery (STREAMS), a technical proof-of-concept implementation, to secure end-to-end sensor data streams using secure software defined networking and elastic compute and storage resources. STREAMS will be the first prototype of a secure network architecture to provide advanced data analytics-based healthcare to stroke patients in a realistic clinical environment. This project addresses issues in securing heterogeneous sensory ePHI patient data. It captures the workflows of patient data analysis and defines a role-based security enforcement framework to apply access policies. A Secure SDN controller will be designed to authenticate, identify, and direct encrypted data streams to ensure the data streaming over the network are HIPAA compliant, provide guidance in provisioning of compute resources at the cloud, and apply the most appropriate decryption algorithms based on the role of users, priority, types and source of the sensor data stream, as well as network conditions. A generalizable secure hardware and software architecture collects, encrypts, decrypts, stores, transports, analyzes, and maintains the integrity and availability of the data from these multimodal sensors to enable them to be fused using analytics algorithms to learn about patient activities that are highly relevant to stroke recovery. The highly interdisciplinary project team consists of healthcare professionals, medical researchers, computer scientists, IT staff, engineering staff, and industrial partners.