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An Energy-Efficient and Secure Data Inference Framework for Internet of Health Things: A Pilot Study

机译：用于健康互联网的节能和安全的数据推断框架：试点研究

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摘要

Privacy protection in electronic healthcare applications is an important consideration, due to the sensitive nature of personal health data. Internet of Health Things (IoHT) networks that are used within a healthcare setting have unique challenges and security requirements (integrity, authentication, privacy, and availability) that must also be balanced with the need to maintain efficiency in order to conserve battery power, which can be a significant limitation in IoHT devices and networks. Data are usually transferred without undergoing filtering or optimization, and this traffic can overload sensors and cause rapid battery consumption when interacting with IoHT networks. This poses certain restrictions on the practical implementation of these devices. In order to address these issues, this paper proposes a privacy-preserving two-tier data inference framework solution that conserves battery consumption by inferring the sensed data and reducing data size for transmission, while also protecting sensitive data from leakage to adversaries. The results from experimental evaluations on efficiency and privacy show the validity of the proposed scheme, as well as significant data savings without compromising data transmission accuracy, which contributes to energy efficiency of IoHT sensor devices.
机译：由于个人健康数据的敏感性，电子医疗保健应用中的隐私保护是一个重要的考虑因素。医疗保健环境中使用的健康互联网（IOHT）网络具有独特的挑战和安全要求（完整性，认证，隐私和可用性），必须平衡，以保持效率以节省电池电量，这可以是IOHT设备和网络中的重大限制。数据通常在不接受过滤或优化的情况下传输，并且该流量可以在与IOHT网络交互时导致电池消耗量快。这对这些设备的实际实现产生了某些限制。为了解决这些问题，本文提出了一种隐私保留的双层数据推断框架解决方案，通过推断感测的数据并减少数据尺寸来保护电池消耗，同时也保护敏感数据从泄漏到对手。关于效率和隐私的实验评估结果表明了拟议方案的有效性，以及显着的数据节省而不影响数据传输精度，这有助于IOHT传感器装置的能效。

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