Your browser doesn't support javascript.
loading
Machine learning cryptography methods for IoT in healthcare.
Chinbat, Tserendorj; Madanian, Samaneh; Airehrour, David; Hassandoust, Farkhondeh.
Afiliación
  • Chinbat T; Department of Computer Science and Software Engineering, Auckland University of Technology (AUT), 6 St. Paul Street, Auckland, 1010, New Zealand.
  • Madanian S; Department of Computer Science and Software Engineering, Auckland University of Technology (AUT), 6 St. Paul Street, Auckland, 1010, New Zealand. sam.madanian@aut.ac.nz.
  • Airehrour D; Together Communications, 77 Cook Street, Auckland, 1010, New Zealand.
  • Hassandoust F; Department of Information Systems and Operation Management, University of Auckland, Auckland CBD, 12 Grafton Road, Auckland, 1010, New Zealand.
BMC Med Inform Decis Mak ; 24(1): 153, 2024 Jun 04.
Article en En | MEDLINE | ID: mdl-38831390
ABSTRACT

BACKGROUND:

The increased application of Internet of Things (IoT) in healthcare, has fueled concerns regarding the security and privacy of patient data. Lightweight Cryptography (LWC) algorithms can be seen as a potential solution to address this concern. Due to the high variation of LWC, the primary objective of this study was to identify a suitable yet effective algorithm for securing sensitive patient information on IoT devices.

METHODS:

This study evaluates the performance of eight LWC algorithms-AES, PRESENT, MSEA, LEA, XTEA, SIMON, PRINCE, and RECTANGLE-using machine learning models. Experiments were conducted on a Raspberry Pi 3 microcontroller using 16 KB to 2048 KB files. Machine learning models were trained and tested for each LWC algorithm and their performance was evaluated based using precision, recall, F1-score, and accuracy metrics.

RESULTS:

The study analyzed the encryption/decryption execution time, energy consumption, memory usage, and throughput of eight LWC algorithms. The RECTANGLE algorithm was identified as the most suitable and efficient LWC algorithm for IoT in healthcare due to its speed, efficiency, simplicity, and flexibility.

CONCLUSIONS:

This research addresses security and privacy concerns in IoT healthcare and identifies key performance factors of LWC algorithms utilizing the SLR research methodology. Furthermore, the study provides insights into the optimal choice of LWC algorithm for enhancing privacy and security in IoT healthcare environments.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Seguridad Computacional / Aprendizaje Automático / Internet de las Cosas Límite: Humans Idioma: En Revista: BMC Med Inform Decis Mak Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Nueva Zelanda Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Seguridad Computacional / Aprendizaje Automático / Internet de las Cosas Límite: Humans Idioma: En Revista: BMC Med Inform Decis Mak Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Nueva Zelanda Pais de publicación: Reino Unido