A privacy-preserving data inference framework for internet of health things networks

James Jin Kang, Mahdi Dibaei, Gang Luo, Wencheng Yang, Xi Zheng

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

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 have privacy requirements within a healthcare setting. However, these networks have unique challenges and security requirements (integrity, authentication, privacy and availability) 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 consequently poses restrictions on the practical implementation of these devices. As a solution to address the issues, this paper proposes a privacy-preserving two-tier data inference framework - this can conserve battery consumption by reducing the data size required to transmit through inferring the sensed data and can also protect the sensitive data from leakage to adversaries. Results from experimental evaluations on privacy show the validity of the proposed scheme as well as significant data savings without compromising the accuracy of the data transmission, which contributes to energy efficiency of IoHT sensor devices.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020
EditorsGuojun Wang, Ryan Ko, Md Zakirul Alam Bhuiyan, Yi Pan
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1209-1214
Number of pages6
ISBN (Electronic)9780738143804
ISBN (Print)9781665403924
DOIs
Publication statusPublished - 2020
Event19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020 - Guangzhou, China
Duration: 29 Dec 20201 Jan 2021

Publication series

NameProceedings - 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020

Conference

Conference19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020
CountryChina
CityGuangzhou
Period29/12/201/01/21

Keywords

  • Body Sensors
  • Cloud
  • Healthcare Big Data
  • Inference System
  • Internet of Health Things (IoHT)
  • IoT
  • MHealth
  • Privacy-Preserving
  • Wireless Body Area Network (WBAN)

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