The DOI matches its Crossref record; 35 of the 38 references it cites were found in a registry or an index.
A Trustworthy Privacy Preserving Framework for Machine Learning in Industrial IoT Systems
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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In the citing paper
“AI systems have the potential to contribute to the resolution of some of society's most urgent issues, but this must be done in the most environmentally friendly approach conceivable [26].”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [26]
In the citing paper
“Smart contracts are useful to build secure communication between the central authority and the distributed users for safe model updating [57].”
From Federated learning meets blockchain in edge computing: Opportunities and challenges (2021) · cites this paper as [57]
In the citing paper
“Most of existing works focus on building attack detection mechanisms for model training and update transmission in FLchain [55], [57], [60].”
From Federated learning meets blockchain in edge computing: Opportunities and challenges (2021) · cites this paper as [57]
Citations
Cites
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Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322
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Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754
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Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961
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Azaria, A., Ekblaw, A., Vieira, T., & Lippman, A. (2016) · 2016 2nd International Conference on Open and Big Data (OBD) · doi:10.1109/obd.2016.11 · Cited by 2,526
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Wang, S., Tuor, T., Salonidis, T., Leung, K. K., Makaya, C., He, T., & Chan, K. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2904348 · Cited by 2,232
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Boyes, H. A., Hallaq, B., Cunningham, J., & Watson, T. (2018) · Computers in Industry · doi:10.1016/j.compind.2018.04.015 · Cited by 1,496
Consortium Blockchain for Secure Energy Trading in Industrial Internet of Things
Li, Z., Kang, J., Yu, R., Ye, D., Deng, Q., & Zhang, Y. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2786307 · Cited by 1,103
Federated Learning of Deep Networks using Model AveragingPreprint
McMahan, H. B., Moore, E., Ramage, D., & y Arcas, B. A. (2016) · arXiv (Cornell University) · Cited by 1,005
Research works printed but not linked: 1
- dwork, 0, The differential privacy frontier, Proc Theory Cryptography Conf, 496
Cited by
Sorted by citations
Federated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3075439 · Cited by 1,503
“[171] Secure industrial IoT DFL DNN IoT devices IoT devices An integrated FL-blockchain scheme for industrial IoT.”
BackgroundMethodInfluentialFederated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Poor, H. (2021) · Deakin Research Online (Deakin University) · Cited by 889
Federated learning meets blockchain in edge computing: Opportunities and challenges
Nguyen, D. C., Ding, M., Pham, Q.-V., Pathirana, P. N., Le, L. B., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3072611 · Cited by 661
Ghimire, B., & Rawat, D. B. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3150363 · Cited by 520
Kumar, S., Lim, W. M., Sivarajah, U., & Kaur, J. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10279-0 · Cited by 441
Metadata
- 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
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2020-09
- Volume
- 16
- Issue
- 9
- Pages
- 6092-6102
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- 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
- 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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