A Machine-Learning–Blockchain-Based Authentication Using Smart Contracts for an IoHT System

Author:

Gaur RajkumarORCID,Prakash Shiva,Kumar SanjayORCID,Abhishek KumarORCID,Msahli Mounira,Wahid AbdulORCID

Abstract

Nowadays, finding genetic components and determining the likelihood that treatment would be helpful for patients are the key issues in the medical field. Medical data storage in a centralized system is complex. Data storage, on the other hand, has recently been distributed electronically in a cloud-based system, allowing access to the data at any time through a cloud server or blockchain-based ledger system. The blockchain is essential to managing safe and decentralized transactions in cryptography systems such as bitcoin and Ethereum. The blockchain stores information in different blocks, each of which has a set capacity. Data processing and storage are more effective and better for data management when blockchain and machine learning are integrated. Therefore, we have proposed a machine-learning–blockchain-based smart-contract system that improves security, reduces consumption, and can be trusted for real-time medical applications. The accuracy and computation performance of the IoHT system are safely improved by our system.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference49 articles.

1. Machine learning adoption in blockchain-based smart applications: The challenges, and a way forward;IEEE Access,2019

2. Security and privacy in IoT using machine learning and blockchain: Threats and countermeasures;ACM Comput. Surv. (CSUR),2020

3. Gaur, R., and Prakash, S. (2021). Innovations in Electrical and Electronic Engineering, Springer.

4. Fast, compact, and expressive attribute-based encryption;Des. Codes Cryptogr.,2021

5. Dunnett, K., Pal, S., and Jadidi, Z. (2022). Challenges and Opportunities of Blockchain for Cyber Threat Intelligence Sharing. arXiv.

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