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Article

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

1
School of Science, Edith Cowan University, Joondalup 6027, Australia
2
Department of Computer Engineering, Islamic Azad University Tabriz Branch, Tabriz 5166616471, Iran
3
Department of Computing, Macquarie University, Sydney 2109, Australia
*
Authors to whom correspondence should be addressed.
Sensors 2021, 21(1), 312; https://doi.org/10.3390/s21010312
Received: 19 November 2020 / Revised: 29 December 2020 / Accepted: 31 December 2020 / Published: 5 January 2021
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. View Full-Text
Keywords: privacy-preserving; body sensors; wireless body area network (WBAN); Internet of Health Things (IoHT); mHealth; IoT; cloud; healthcare big data; inference system privacy-preserving; body sensors; wireless body area network (WBAN); Internet of Health Things (IoHT); mHealth; IoT; cloud; healthcare big data; inference system
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MDPI and ACS Style

Kang, J.J.; Dibaei, M.; Luo, G.; Yang, W.; Haskell-Dowland, P.; Zheng, X. An Energy-Efficient and Secure Data Inference Framework for Internet of Health Things: A Pilot Study. Sensors 2021, 21, 312. https://doi.org/10.3390/s21010312

AMA Style

Kang JJ, Dibaei M, Luo G, Yang W, Haskell-Dowland P, Zheng X. An Energy-Efficient and Secure Data Inference Framework for Internet of Health Things: A Pilot Study. Sensors. 2021; 21(1):312. https://doi.org/10.3390/s21010312

Chicago/Turabian Style

Kang, James J., Mahdi Dibaei, Gang Luo, Wencheng Yang, Paul Haskell-Dowland, and Xi Zheng. 2021. "An Energy-Efficient and Secure Data Inference Framework for Internet of Health Things: A Pilot Study" Sensors 21, no. 1: 312. https://doi.org/10.3390/s21010312

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