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

James Jin Kang1, Mahdi Dibaei2, Gang Luo3

  • 1School of Science, Edith Cowan University, Joondalup 6027, Australia.

Sensors (Basel, Switzerland)
|January 20, 2021
PubMed
Summary

This study introduces a privacy-preserving framework for Internet of Health Things (IoHT) networks. It reduces data size for efficient transmission and conserves battery power while protecting sensitive health information.

Keywords:
Internet of Health Things (IoHT)IoTbody sensorscloudhealthcare big datainference systemmHealthprivacy-preservingwireless body area network (WBAN)

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Area of Science:

  • Healthcare Technology
  • Cybersecurity
  • Wireless Sensor Networks

Background:

  • Electronic healthcare applications handle sensitive personal health data, necessitating robust privacy protection.
  • Internet of Health Things (IoHT) networks face challenges balancing security requirements (integrity, authentication, privacy, availability) with efficiency and battery conservation.
  • Unfiltered data transmission in IoHT can overload sensors and deplete battery power, limiting practical implementation.

Purpose of the Study:

  • To propose a novel privacy-preserving, two-tier data inference framework for IoHT networks.
  • To address the dual challenges of data privacy and energy efficiency in IoHT devices.
  • To reduce data size for transmission, thereby conserving battery power and enhancing network efficiency.

Main Methods:

  • Development of a two-tier data inference framework.
  • Implementation of data inference techniques to reduce data size before transmission.
  • Integration of privacy-preserving mechanisms to protect sensitive health data from adversaries.

Main Results:

  • Experimental evaluations demonstrate the validity of the proposed framework.
  • Significant data savings were achieved without compromising data transmission accuracy.
  • The scheme contributes to enhanced energy efficiency for IoHT sensor devices.

Conclusions:

  • The proposed framework effectively balances privacy protection and energy efficiency in IoHT networks.
  • Data inference reduces transmission load, leading to improved battery life and network performance.
  • The solution is validated for practical implementation in electronic healthcare applications.