Document Type

Journal Article

Publication Title

Sensors

Volume

21

Issue

1

First Page

1

Last Page

17

PubMed ID

33466416

Publisher

MDPI

School

School of Science

RAS ID

32589

Comments

Kang, J. J., Dibaei, M., Luo, G., Yang, W., Haskell-Dowland, P., & Zheng, X. (2021). An energy-efficient and secure data inference framework for internet of health things: A pilot study. Sensors, 21(1), article 312. https://doi.org/10.3390/s21010312

Abstract

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. 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.

DOI

10.3390/s21010312

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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