Machine Learning Cryptography Methods for IoT in Healthcare

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aut.relation.articlenumber153
aut.relation.issue1
aut.relation.journalBMC Med Inform Decis Mak
aut.relation.startpage153
aut.relation.volume24
dc.contributor.authorChinbat, Tserendorj
dc.contributor.authorMadanian, Samaneh
dc.contributor.authorAirehrour, David
dc.contributor.authorHassandoust, Farkhondeh
dc.date.accessioned2024-06-12T01:23:40Z
dc.date.available2024-06-12T01:23:40Z
dc.date.issued2024-06-04
dc.description.abstractBACKGROUND: 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.
dc.identifier.citationBMC Med Inform Decis Mak, ISSN: 1472-6947 (Online), Springer Science and Business Media LLC, 24(1), 153-. doi: 10.1186/s12911-024-02548-6
dc.identifier.doi10.1186/s12911-024-02548-6
dc.identifier.issn1472-6947
dc.identifier.urihttp://hdl.handle.net/10292/17647
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.relation.urihttps://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-024-02548-6
dc.rightsOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
dc.rights.accessrightsOpenAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectCryptography algorithms
dc.subjectDigital health
dc.subjectInternet of Things
dc.subjectIoT
dc.subject0806 Information Systems
dc.subject1103 Clinical Sciences
dc.subjectMedical Informatics
dc.subject4203 Health services and systems
dc.subject.meshMachine Learning
dc.subject.meshHumans
dc.subject.meshComputer Security
dc.subject.meshInternet of Things
dc.subject.meshAlgorithms
dc.subject.meshConfidentiality
dc.titleMachine Learning Cryptography Methods for IoT in Healthcare
dc.typeJournal Article
pubs.elements-id556106
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