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Emerging Security Mechanisms for Medical Cyber Physical Systems

机译:医疗网络物理系统的新兴安全机制

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The following decade will witness a surge in remote health-monitoring systems that are based on body-worn monitoring devices. These Medical Cyber Physical Systems (MCPS) will be capable of transmitting the acquired data to a private or public cloud for storage and processing. Machine learning algorithms running in the cloud and processing this data can provide decision support to healthcare professionals. There is no doubt that the security and privacy of the medical data is one of the most important concerns in designing an MCPS. In this paper, we depict the general architecture of an MCPS consisting of four layers: data acquisition, data aggregation, cloud processing, and action. Due to the differences in hardware and communication capabilities of each layer, different encryption schemes must be used to guarantee data privacy within that layer. We survey conventional and emerging encryption schemes based on their ability to provide secure storage, data sharing, and secure computation. Our detailed experimental evaluation of each scheme shows that while the emerging encryption schemes enable exciting new features such as secure sharing and secure computation, they introduce several orders-of-magnitude computational and storage overhead. We conclude our paper by outlining future research directions to improve the usability of the emerging encryption schemes in an MCPS.
机译:在接下来的十年中,基于人体监测设备的远程健康监测系统将会激增。这些医学网络物理系统(MCPS)将能够将获取的数据传输到私有或公共云进行存储和处理。在云中运行并处理该数据的机器学习算法可以为医疗保健专业人员提供决策支持。毫无疑问,医疗数据的安全性和保密性是设计MCPS时最重要的问题之一。在本文中,我们描述了MCPS的一般体系结构,该体系结构由四层组成:数据获取,数据聚合,云处理和操作。由于每一层在硬件和通信能力上的差异,必须使用不同的加密方案来保证该层内的数据隐私。我们根据常规和新兴的加密方案提供安全存储,数据共享和安全计算的能力来进行调查。我们对每种方案的详细实验评估表明,尽管新兴的加密方案启用了令人兴奋的新功能(例如安全共享和安全计算),但它们却引入了几个数量级的计算和存储开销。在总结本文时,我们概述了未来的研究方向,以改进MCPS中新兴加密方案的可用性。

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