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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215
Deep Learning with Differential Privacy
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
Membership Inference Attacks Against Machine Learning Models
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
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
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
MedRec: Using Blockchain for Medical Data Access and Permission Management
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
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
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
The industrial internet of things (IIoT): An analysis framework
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
The Scyther Tool: Verification, Falsification, and Analysis of Security Protocols
Cremers, C. J. F. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-70545-1_38 · Cited by 821
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds
Bun, M., & Steinke, T. (2016) · Lecture notes in computer science · doi:10.1007/978-3-662-53641-4_24 · Cited by 703
A hierarchy of authentication specifications
Lowe, G. (2002) · IEEE Computer Security Foundations Workshop (CSFW) · doi:10.1109/csfw.1997.596782 · Cited by 629
Blockchain and Smart Contracts for Insurance: Is the Technology Mature Enough?
Gatteschi, V., Lamberti, F., Demartini, C. G., Pranteda, C., & Santamaría, V. (2018) · Future Internet · doi:10.3390/fi10020020 · Cited by 532
Machine Learning Models that Remember Too Much
Song, C., Ristenpart, T., & Shmatikov, V. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134077 · Cited by 526
Hassan, M. U., Rehmani, M. H., & Chen, J. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.02.060 · Cited by 520
A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT
Lu, R., Heung, K., Lashkari, A. H., & Ghorbani, A. A. (2017) · IEEE Access · doi:10.1109/access.2017.2677520 · Cited by 478
Extremal Mechanisms for Local Differential PrivacyPreprint
KairouzPeter, OhSewoong, & ViswanathPramod (2014) · arXiv (Cornell University) · doi:10.5555/2946645.2946662 · Cited by 414
Automatic fruit classification using deep learning for industrial applications
Hossain, M. S., Al-Hammadi, M., & Muhammad, G. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2875149 · Cited by 347
A Blockchain-Based Solution for Enhancing Security and Privacy in Smart Factory
Wan, J., Li, J., Imran, M. A., Li, D., & Amin, F.-E. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2894573 · Cited by 347
Differentially private data release for data mining
Mohammed, N., Chen, R., Fung, B. C. M., & Yu, P. S. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020487 · Cited by 338
Heavy Hitter Estimation over Set-Valued Data with Local Differential Privacy
Qin, Z., Yang, Y., Yu, T., Khalil, I. M., Xiao, X., & Ren, K. (2016) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2976749.2978409 · Cited by 334
Self-citationLocal Differential Privacy for Deep Learning
Arachchige, P. C. M., Bertök, P., Khalil, I., Liu, D., Camtepe, S. A., & Atiquzzaman, M. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2952146 · Cited by 319
Self-citationFault Detection and Isolation in Industrial Processes Using Deep Learning Approaches
Iqbal, R., Maniak, T., Doctor, F., & Karyotis, C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2902274 · Cited by 221
Shevchik, S. A., Masinelli, G., Kenel, C., Leinenbach, C., & Wasmer, K. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2910524 · Cited by 199
IDARTS – Towards intelligent data analysis and real-time supervision for industry 4.0
Peres, R. S., Rocha, A. D., Leitão, P., & Barata, J. (2018) · Computers in Industry · doi:10.1016/j.compind.2018.07.004 · Cited by 197
IoT data privacy via blockchains and IPFS
Ali, M. S., Dolui, K., & Antonelli, F. (2017) · International Conference on the Internet of Things (IoT) · doi:10.1145/3131542.3131563 · Cited by 191
The Differential Privacy Frontier (Extended Abstract)
Dwork, C. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-00457-5_29 · Cited by 136
Blockchain-based platform architecture for industrial IoT
Teslya, N., & Ryabchikov, I. (2017) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct.2017.8250199 · Cited by 129
Using Randomized Response for Differential Privacy Preserving Data Collection
Wang, Y., Wu, X., & Hu, D. (2016) · Cited by 112
An efficient and scalable privacy preserving algorithm for big data and data streams
Chamikara, M. A. P., Bertök, P., Liu, D., Camtepe, S. A., & Khalil, I. (2019) · Computers & Security · doi:10.1016/j.cose.2019.101570 · Cited by 66
Self-citationTrustworthy industrial IoT gateways for interoperability platforms and ecosystems
Fraile, F., Tagawa, T., Poler, R., & Ortiz, A. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2832041 · Cited by 57
Framework for Improving Critical Infrastructure Cybersecurity, Version 1.0
Stine, K. (2014) · doi:10.6028/nist.cswp.1 · Cited by 36
Chanyaswad, T., Dytso, A., Poor, H. V., & Mittal, P. (2018) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3243734.3243750 · Cited by 34
Cremers, C. J. F., & Mauw, S. (2012) · Information security and cryptography · doi:10.1007/978-3-540-78636-8_4 · Cited by 7
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
