Cited by 236Blockchain Technology Applications and SecurityPrivacy-Preserving Technologies in DataCryptography and Data Security

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A Trustworthy Privacy Preserving Framework for Machine Learning in Industrial IoT Systems

Arachchige, Pathum Chamikara Mahawaga, Bertok, Peter, Khalil, Ibrahim, Liu, Dongxi, Camtepe, Seyit, Atiquzzaman, Mohammed·2020·IEEE Transactions on Industrial Informatics

DOI 10.1109/tii.2020.2974555

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Abstract

Industrial Internet of Things (IIoT) is revolutionizing many leading industries such as energy, agriculture, mining, transportation, and healthcare. IIoT is a major driving force for Industry 4.0, which heavily utilizes machine learning (ML) to capitalize on the massive interconnection and large volumes of IIoT data. However, ML models that are trained on sensitive data tend to leak privacy to adversarial attacks, limiting its full potential in Industry 4.0. This article introduces a framework named PriModChain that enforces privacy and trustworthiness on IIoT data by amalgamating differential privacy, federated ML, Ethereum blockchain, and smart contracts. The feasibility of PriModChain in terms of privacy, security, reliability, safety, and resilience is evaluated using simulations developed in Python with socket programming on a general-purpose computer. We used Ganache_v2.0.1 local test network for the local experiments and Kovan test network for the public blockchain testing. We verify the proposed security protocol using Scyther_v1.1.3 protocol verifier.

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Authors: Arachchige, P. C. M., Bertok, P., Khalil, I., Liu, D., Camtepe, S., & Atiquzzaman, M.
Affiliations: RMIT University (AU)
Commonwealth Scientific and Industrial Research Organisation (AU)
University of Oklahoma (US)
ORCID: Arachchige, Pathum Chamikara Mahawaga
Khalil, Ibrahim
Liu, Dongxi
Camtepe, Seyit
Atiquzzaman, Mohammed
Venue: IEEE Transactions on Industrial Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020-09
Volume: 16
Issue: 9
Pages: 6092-6102
ISSN: 1551-3203 (ISSN-L)
1941-0050
DOI: 10.1109/tii.2020.2974555
OpenAlex: W3008569902
Cited by: 236
References: 38
Citations by year: 2026: 11 · 2025: 33 · 2024: 46 · 2023: 50 · 2022: 43 · 2021: 41 · 2020: 12
FWCI: 34.34
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Industrial Informatics (publishedVersion)
RMIT Research Repository (RMIT University Library) (submittedVersion)
Figshare (submittedVersion)
Topics: Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
Keywords: Industrial Internet of Things, privacy preservation, differential privacy, federated learning, Ethereum, smart contracts, adversarial attacks, Industry 4.0, security protocol verification, Scyther, Ganache

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A Trustworthy Privacy Preserving Framework for Machine Learning in Industrial IoT Systems

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