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Smart Partitioned Blockchain.

Basem Assiri1, Hani Alnami1

  • 1Computer Science Department, Jazan University, Jazan 82917, Saudi Arabia.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
Summary
This summary is machine-generated.

A novel smart partitioned blockchain model customizes blockchain for diverse applications like IoT and smart spaces. Machine learning classifies transactions by sensitivity, optimizing data processing and storage with high accuracy for critical financial and medical data.

Keywords:
Internet of Thingsblockchaindata sensitivitydeep learningmachine learningpartitioned blockchainsecuritysmart spaces

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Area of Science:

  • Computer Science
  • Information Technology
  • Machine Learning

Background:

  • Blockchain technology offers potential advancements across various fields.
  • Integrating blockchain with smart spaces and the Internet of Things (IoT) presents challenges due to diverse transaction sensitivities (correctness, time, specialization).
  • Existing blockchain models may not adequately address the nuanced requirements of sensitive data processing in interconnected systems.

Purpose of the Study:

  • To introduce a customized blockchain model, the smart partitioned blockchain, for smart spaces and IoT applications.
  • To leverage machine learning and deep learning for classifying transactions based on sensitivity levels.
  • To demonstrate the model's adaptability and effectiveness in meeting specific application requirements.

Main Methods:

  • Development of the smart partitioned blockchain model.
  • Utilizing machine learning (Random Forest) and deep learning (Sequential Deep Learning) for transaction classification.
  • Mapping classified transaction pools to specific, potentially permissioned or permissionless, parts of the blockchain architecture.
  • Experimental validation using bank and medical datasets with predefined sensitivity thresholds.

Main Results:

  • The Random Forest model achieved 100% accuracy in classifying critical bank transactions.
  • Sequential Deep Learning achieved 91% accuracy for classifying speculative medical transactions.
  • The smart partitioned blockchain model successfully mapped transactions to appropriate blockchain segments based on sensitivity.
  • Acceptability of classification was evaluated against predefined sensitivity thresholds for each dataset.

Conclusions:

  • The smart partitioned blockchain model offers a flexible and customizable solution for integrating blockchain with sensitive IoT and smart space data.
  • Machine learning and deep learning are effective tools for managing transaction sensitivity in blockchain applications.
  • The model's performance demonstrates its potential for secure and efficient data handling in critical sectors like finance and healthcare.