A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services
1
School of Computer Science, The University of Sydney, Darlington, NSW 2008, Australia
2
School of Engineering and Technology, Central Queensland University, Sydney, 2000 NSW, Australia
3
School of Computer, Data and Mathematical Sciences, Western Sydney University, Kingswood, NSW 2747, Australia
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(2), 552; https://doi.org/10.3390/s21020552
Received: 3 November 2020
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Revised: 7 January 2021
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Accepted: 8 January 2021
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Published: 14 January 2021
(This article belongs to the Special Issue Machine Learning for IoT Applications and Digital Twins)
This paper proposes a novel identity management framework for Internet of Things (IoT) and cloud computing-based personalized healthcare systems. The proposed framework uses multimodal encrypted biometric traits to perform authentication. It employs a combination of centralized and federated identity access techniques along with biometric based continuous authentication. The framework uses a fusion of electrocardiogram (ECG) and photoplethysmogram (PPG) signals when performing authentication. In addition to relying on the unique identification characteristics of the users’ biometric traits, the security of the framework is empowered by the use of Homomorphic Encryption (HE). The use of HE allows patients’ data to stay encrypted when being processed or analyzed in the cloud. Thus, providing not only a fast and reliable authentication mechanism, but also closing the door to many traditional security attacks. The framework’s performance was evaluated and validated using a machine learning (ML) model that tested the framework using a dataset of 25 users in seating positions. Compared to using just ECG or PPG signals, the results of using the proposed fused-based biometric framework showed that it was successful in identifying and authenticating all 25 users with 100% accuracy. Hence, offering some significant improvements to the overall security and privacy of personalized healthcare systems.
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Keywords:
identity management; personalized healthcare; authentication; cloud computing; internet of things; fused-based biometric; machine learning; security; privacy; cybersecurity
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MDPI and ACS Style
Farid, F.; Elkhodr, M.; Sabrina, F.; Ahamed, F.; Gide, E. A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services. Sensors 2021, 21, 552. https://doi.org/10.3390/s21020552
AMA Style
Farid F, Elkhodr M, Sabrina F, Ahamed F, Gide E. A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services. Sensors. 2021; 21(2):552. https://doi.org/10.3390/s21020552
Chicago/Turabian StyleFarid, Farnaz, Mahmoud Elkhodr, Fariza Sabrina, Farhad Ahamed, and Ergun Gide. 2021. "A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services" Sensors 21, no. 2: 552. https://doi.org/10.3390/s21020552
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