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Performance Evaluation of Lightweight Cryptographic Algorithms for IoT in Healthcare

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Thesis(2.57 MB)

Date

2023

Authors

Chinbat, Tserendorj

Supervisor

Madanian, Sam

Item type

Thesis

Degree name

Master of Cyber Security and Digital Forensics

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Publisher

Auckland University of Technology

Abstract

The significant number of objects interconnected to the Internet has grown as the Internet has become a more critical aspect of today's society. Network access, such as the Internet, is no longer restricted to personal computers, laptops, or data centers. It is now found in household items, automobiles, camera systems, implanted devices, as well as other items. The healthcare industry has challenged securing and effectively collecting patients' medical data. Medical data is gathered from the patient's body utilising sensors or Internet of Things (IoT) devices, and securely transmitted to the healthcare system. Even though these medical devices are connected to the network, they must follow Information Security standards, such as confidentiality, integrity, and availability to secure their data. Furthermore, the healthcare service must respect patients' privacy and ensure sufficient protection for their data and information. To avoid unauthorised access, it is necessary to establish data confidentiality from the beginning of the clinical treatment. As a result, medical data encryption is required from IoT medical devices, however, because of the limitations in their power, memory, and processor speed where traditional cryptographic algorithms are recognised as totally impractical. This has resulted in Lightweight Cryptography (LWC) compared to other traditional encryption methods, which can perform in devices that have limited resources, such as IoT medical devices. This study has three main points. The first is to investigate potential IoT privacy and security issues in healthcare. And the second is to determine the most critical performance factors of LWC algorithms for IoT medical devices. Finally, the last main point is to evaluate the performance results of selected LWC algorithms using their experimental performance test results. The study determines the best candidate LWC algorithm for the healthcare system.

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https://hdl.handle.net/10292/15854

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Masters Theses