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

  • Rajkumar Gaur
  • , Shiva Prakash
  • , Sanjay Kumar
  • , Kumar Abhishek
  • , Mounira Msahli
  • , Abdul Wahid

Research output: Contribution to a Journal (Peer & Non Peer)Articlepeer-review

25 Citations (Scopus)

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.

Original languageEnglish
Article number9074
JournalSensors (Switzerland)
Volume22
Issue number23
DOIs
Publication statusPublished - Dec 2022
Externally publishedYes

Keywords

  • CP-ABE
  • IoHT
  • ML-based
  • SVM
  • blockchain
  • secure system
  • smart contract
  • training set

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