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Published July 31, 2023 | Version v1
Journal article Open

A Privacy Protection Framework for Machine Learning for IIOT

Authors/Creators

  • 1. Professor, Department of CSE, AIT, Tumakuru, India -572106
  • 2. PG Student, Department of CSE, AIT, Tumakuru, India-572106

Description

Energy, agriculture, mining, transportation, and healthcare are just a few of the top industries being transformed by the industrial internet of things (IIoT). Industry 4.0, which chiefly depends on AI (ML) to take utilization of the tremendous interconnectedness and huge volumes of IIoT information, is basically determined by IIoT.  To be fully utilised in Industry 4.0, ML models that are trained on sensitive data sometimes leak privacy to adversarial attacks. This paper presents PriMod Chain, a system that upholds security and dependability on IIoT information by consolidating differential protection, united ML, Ethereum block chain, and shrewd agreements.PriMod Chain is a privacy-preserving trustworthy machine learning model training and sharing framework based on blockchain. Reenactments made in Python using attachment programming on a broadly useful PC were utilized to evaluate the practicality of PriMod Chain regarding protection, security, steadfastness, wellbeing, and strength. For the neighborhood review, we utilized the Ganache v2.0.1 nearby test organization, and for the public blockchain testing, we utilized the Kovan test organization. Using the Scyther v1.1.3 protocol verifier, we examined the suggested security protocol.

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