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

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

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

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1109/tii.2020.2974555

Cites

35 of 35 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

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

Show all 38 references

Research works printed but not linked: 1

dwork, 0, The differential privacy frontier, Proc Theory Cryptography Conf, 496

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.”
BackgroundMethodInfluential
Federated 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
Recent Advances on Federated Learning for Cybersecurity and Cybersecurity for Federated Learning for Internet of Things
Ghimire, B., & Rawat, D. B. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3150363 · Cited by 520
Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis
Kumar, S., Lim, W. M., Sivarajah, U., & Kaur, J. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10279-0 · Cited by 441
PPSF: a privacy-preserving and secure framework using blockchain-based machine-learning for IoT-driven smart cities
Kumar, P., Kumar, R., Srivastava, G., Gupta, G. P., Tripathi, R., Gadekallu, T. R., & Xiong, N. N. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3089435 · Cited by 360
A Review of Trustworthy and Explainable Artificial Intelligence (XAI)
Chamola, V., Hassija, V., Sulthana, A. R., Ghosh, D., Dhingra, D., & Sikdar, B. (2023) · IEEE Access · doi:10.1109/access.2023.3294569 · Cited by 352
“…The associate editor coordinating the review of this manuscript and approving it for publication was Zijian Zhang . smart assistants, recommendation engines, disease detection, or automated robots, people’s lives are greatly influenced by AI breakthroughs in a variety of sectors [3], [4], [5], [6].”
Background
Privacy-preserving blockchain-based federated learning for traffic flow prediction
Qi, Y., Hossain, M. S., Nie, J., & Li, X. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.12.003 · Cited by 339
Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0
Wu, Y., Dai, H.-N., & Wang, H. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3025916 · Cited by 323
Deep Learning in the Industrial Internet of Things: Potentials, Challenges, and Emerging Applications
Khalil, R. A., Saeed, N., Masood, M., Fard, Y. M., Alouini, M., & Al-Naffouri, T. Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3051414 · Cited by 342
A Systematic Review of Human–Computer Interaction and Explainable Artificial Intelligence in Healthcare With Artificial Intelligence Techniques
Nazar, M., Alam, M. M., Yafi, E., & Su'ud, M. M. (2021) · IEEE Access · doi:10.1109/access.2021.3127881 · Cited by 312
A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions
Aouedi, O., Vu, T.-H., Sacco, A., Nguyen, D. C., Piamrat, K., Marchetto, G., & Pham, Q. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3430368 · Cited by 345
Fusion of Federated Learning and Industrial Internet of Things: A survey
Boobalan, P., Ramu, S. P., Pham, Q., Dev, K., Pandya, S., Maddikunta, P. K. R., Gadekallu, T. R., & Huynh‐The, T. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109048 · Cited by 321
Deep learning enabled intrusion detection system for Industrial IOT environment
Nandanwar, H., & Katarya, R. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123808 · Cited by 258
Differential Privacy for Industrial Internet of Things: Opportunities, Applications, and Challenges
Jiang, B., Li, J., Yue, G., & Song, H. H. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3057419 · Cited by 204
BEdgeHealth: A Decentralized Architecture for Edge-Based IoMT Networks Using Blockchain
Nguyen, D. C., Pathirana, P. N., Ding, M., & Seneviratne, A. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3058953 · Cited by 184
A deep learning approach for intrusion detection in Internet of Things using focal loss function
Dina, A. S., Siddique, A. B., & Manivannan, D. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100699 · Cited by 173
Privacy Preserving Face Recognition Utilizing Differential Privacy
Chamikara, M. A. P., Bertök, P., Khalil, I., Liu, D., & Camtepe, S. A. (2020) · Computers & Security · doi:10.1016/j.cose.2020.101951 · Cited by 161
Self-citation
Disaster and Pandemic Management Using Machine Learning: A Survey
Chamola, V., Hassija, V., Gupta, S., Goyal, A., Guizani, M., & Sikdar, B. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3044966 · Cited by 150
Privacy preserving distributed machine learning with federated learning
Chamikara, M. A. P., Bertök, P., Khalil, I., Liu, D., & Camtepe, S. A. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.02.014 · Cited by 148
Self-citation
Federated Learning and Blockchain-Enabled Fog-IoT Platform for Wearables in Predictive Healthcare
Baucas, M. J., Spachos, P., & Plataniotis, K. N. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3235950 · Cited by 154
Secure Data Transmission and Trustworthiness Judgement Approaches Against Cyber-Physical Attacks in an Integrated Data-Driven Framework
Jiang, Y., Wu, S., Yang, H., Luo, H., Chen, Z., Yin, S., & Kaynak, O. (2022) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2022.3164024 · Cited by 127
Blockchain-Based Internet of Things and Industrial IoT: A Comprehensive Survey
Dwivedi, S. K., Roy, P., Karda, C., Agrawal, S., & Amin, R. (2021) · Security and Communication Networks · doi:10.1155/2021/7142048 · Cited by 122
Healthcare Internet of Things: Security Threats, Challenges, and Future Research Directions
Adil, M., Khan, M. K., Kumar, N., Attique, M., Farouk, A., Guizani, M., & Jin, Z. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3360289 · Cited by 120
Advancements of Federated Learning Towards Privacy Preservation: From Federated Learning to Split Learning
Thapa, C., Chamikara, M. A. P., & Camtepe, S. A. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-70604-3_4 · Cited by 111
Self-citation
Deep reinforcement learning for blockchain in industrial IoT: A survey
Wu, Y., Wang, Z., Ma, Y., & Leung, V. C. M. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108004 · Cited by 92
Federated learning for 6G-enabled secure communication systems: a comprehensive survey
Sirohi, D., Kumar, N., Rana, P. S., Tanwar, S., Iqbal, R., & Hijji, M. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10417-3 · Cited by 100
Privacy-Preserving Federated Learning for Intrusion Detection in IoT Environments: A Survey
Vyas, A., Lin, P., Hwang, R., & Tripathi, M. (2024) · IEEE Access · doi:10.1109/access.2024.3454211 · Cited by 115
Trustworthy Federated Learning: A Comprehensive Review, Architecture, Key Challenges, and Future Research Prospects
Tariq, A., Serhani, M. A., Sallabi, F., Barka, E. S., Qayyum, T., Khater, H. M., & Shuaib, K. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3438264 · Cited by 130
Using data mining techniques to explore security issues in smart living environments in Twitter
Saura, J. R., Palacios‐Marqués, D., & Soriano, D. R. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.08.021 · Cited by 88
Privacy-preserving in Blockchain-based Federated Learning systems
Sameera, K. M., Nicolazzo, S., Arazzi, M., Nocera, A., A., R. R. K., Vinod, P., & Conti, M. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.04.024 · Cited by 107
A systematic review of privacy-preserving methods deployed with blockchain and federated learning for the telemedicine
Hiwale, M., Walambe, R. A., Potdar, V. M., & Kotecha, K. V. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100192 · Cited by 94
A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things
Rodríguez, E., Otero, B., & Canal, R. (2023) · Sensors · doi:10.3390/s23031252 · Cited by 82
A Survey on Security of UAV Swarm Networks: Attacks and Countermeasures
Wang, X., Zhao, Z., Yi, L., Ning, Z., Guo, L., Yu, F. R., & Guo, S. (2024) · ACM Computing Surveys · doi:10.1145/3703625 · Cited by 77
Machine Learning-Based Security Solutions for IoT Networks: A Comprehensive Survey
Alfahaid, A., Alalwany, E., Almars, A. M., Alharbi, F., Atlam, E.-S., & Mahgoub, I. (2025) · Sensors · doi:10.3390/s25113341 · Cited by 89
Trustworthy Privacy-Preserving Hierarchical Ensemble and Federated Learning in Healthcare 4.0 With Blockchain
Stephanie, V., Khalil, I., Atiquzzaman, M., & Yi, X. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3214998 · Cited by 82
Self-citation
Recent endeavors in machine learning-powered intrusion detection systems for the Internet of Things
Manivannan, D. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.103925 · Cited by 78
How might technology rise to the challenge of data sharing in agri-food?
Durrant, A., Marković, M., Matthews, D., May, D., Leontidis, G., & Enright, J. A. (2021) · Global Food Security · doi:10.1016/j.gfs.2021.100493 · Cited by 74
Machine Learning for Emergency Management: A Survey and Future Outlook
Kyrkou, C., Kolios, P., Theocharides, T., & Polycarpou, M. M. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3223186 · Cited by 78
Two-Level Privacy-Preserving Framework: Federated Learning for Attack Detection in the Consumer Internet of Things
Rabieinejad, E., Yazdinejad, A., Dehghantanha, A., & Srivastava, G. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3349490 · Cited by 73
On Blockchain and IoT Integration Platforms: Current Implementation Challenges and Future Perspectives
Nartey, C., Tchao, E. T., Gadze, J. D., Keelson, E., Klogo, G. S., Kommey, B., & Diawuo, K. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/6672482 · Cited by 77
Understanding blockchain applications in Industry 4.0: From information technology to manufacturing and operations management
Nuttah, M. M., Roma, P., Nigro, G. L., & Perrone, G. (2023) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2023.100456 · Cited by 80
Securing Industry 5.0: An explainable deep learning model for intrusion detection in cyber-physical systems
Nandanwar, H., & Katarya, R. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110161 · Cited by 81
Differential Privacy for IoT-Enabled Critical Infrastructure: A Comprehensive Survey
Husnoo, M. A., Anwar, A., Chakrabortty, R. K., Doss, R., & Ryan, M. J. (2021) · IEEE Access · doi:10.1109/access.2021.3124309 · Cited by 74
Optical network security management: requirements, architecture, and efficient machine learning models for detection of evolving threats [Invited]
Furdek, M., Natalino, C., Giglio, A. D., & Schiano, M. (2020) · Journal of Optical Communications and Networking · doi:10.1364/jocn.402884 · Cited by 67
Privacy-Preserved Cyberattack Detection in Industrial Edge of Things (IEoT): A Blockchain-Orchestrated Federated Learning Approach
Abdel‐Basset, M., Moustafa, N., & Hawash, H. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3167663 · Cited by 71
Privacy-Preserved Credit Data Sharing Integrating Blockchain and Federated Learning for Industrial 4.0
Yang, F., Qiao, Y., Abedin, M. Z., & Huang, C. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3151917 · Cited by 65
A Systematic Literature Review of Blockchain-based Federated Learning: Architectures, Applications and Issues
Hou, D., Zhang, J., Man, K. L., Ma, J., & Peng, Z. (2021) · Information Communication Technologies Conference (ICTC) · doi:10.1109/ictc51749.2021.9441499 · Cited by 62
A Binocular Vision Application in IoT: Realtime Trustworthy Road Condition Detection System in Passable Area
Xie, Q., Hu, X., Ren, L., Qi, L., & Sun, Z. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3145858 · Cited by 62
Resource Allocation for Latency-Aware Federated Learning in Industrial Internet of Things
Gao, W.-F., Zhao, Z.-W., Min, G., Ni, Q., & Jiang, Y. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3073642 · Cited by 53
Cooperative Robotics and Machine Learning for Smart Manufacturing: Platform Design and Trends Within the Context of Industrial Internet of Things
Lins, R. G., & Givigi, S. N. (2021) · IEEE Access · doi:10.1109/access.2021.3094374 · Cited by 59
Secure and Scalable Blockchain-Based Federated Learning for Cryptocurrency Fraud Detection: A Systematic Review
Ahmed, A. A., & Alabi, O. O. (2024) · IEEE Access · doi:10.1109/access.2024.3429205 · Cited by 59
Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues
Du, Y., Wang, Z., & Leung, V. C. M. (2021) · Future Internet · doi:10.3390/fi13020048 · Cited by 55
A Verifiable Privacy-Preserving Machine Learning Prediction Scheme for Edge-Enhanced HCPSs
Li, X., He, J., Vijayakumar, P., Zhang, X., & Chang, V. I. C. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3110808 · Cited by 53
Trustworthy artificial intelligence in Alzheimer’s disease: state of the art, opportunities, and challenges
El–Sappagh, S., Alonso, J. M., Abuhmed, T., Ali, F., & Bugarín, A. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10415-5 · Cited by 58
An Overview of Privacy Dimensions on the Industrial Internet of Things (IIoT)
Demertzi, V., Demertzis, S., & Demertzis, K. (2023) · Algorithms · doi:10.3390/a16080378 · Cited by 52
Privacy protection federated learning system based on blockchain and edge computing in mobile crowdsourcing
Wang, W., Wang, Y., Huang, Y., Mu, C., Sun, Z., Tong, X., & Cai, Z. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109206 · Cited by 51
A smart privacy preserving framework for industrial IoT using hybrid meta-heuristic algorithm
Kumar, M., Mukherjee, P., Verma, S., Kavita, K., Shafi, J., Woźniak, M., & Ijaz, M. F. (2023) · Scientific Reports · doi:10.1038/s41598-023-32098-2 · Cited by 54
A novel momentum prototypical neural network to cross-domain fault diagnosis for rotating machinery subject to cold-start
Chen, X., Yang, R., Xue, Y., Yang, C., Song, B., & Zhong, M. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126656 · Cited by 49
Reinforcing Industry 4.0 With Digital Twins and Blockchain-Assisted Federated Learning
Aloqaily, M., Ridhawi, I. A., & Kanhere, S. S. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3310068 · Cited by 49
Federated learning for big data: A survey on opportunities, applications, and future directions
Gadekallu, T. R., Pham, Q., Huynh‐The, T., Feng, H., Fang, K., Pandya, S., Liyanage, M., Wang, W. W., & Nguyen, T. T. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113614 · Cited by 86
A bio-inspired privacy-preserving framework for healthcare systems
Dhasarathan, C., Kumar, M., Srivastava, A. K., Al-Turjman, F. M., Shankar, A., & Kumar, M. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-03720-9 · Cited by 46
AI-Enabled IIoT for Live Smart City Event Monitoring
Rahman, M. A., Hossain, M. S., Showail, A. J., Alrajeh, N. A., & Ghoneim, A. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3109435 · Cited by 46
Blockchain-Assisted Hierarchical Attribute-Based Encryption Scheme for Secure Information Sharing in Industrial Internet of Things
Sasikumar, A., Ravi, L., Devarajan, M., Selvalakshmi, A., Almaktoom, A. T., Almazyad, A. S., Xiong, G., & Mohamed, A. W. (2024) · IEEE Access · doi:10.1109/access.2024.3354846 · Cited by 45
Symbiotic Relationship Between Machine Learning and Industry 4.0: A Review
Azeem, M., Haleem, A., & Javaid, M. (2021) · Journal of Industrial Integration and Management · doi:10.1142/s2424862221300027 · Cited by 46
A Checkpoint Enabled Scalable Blockchain Architecture for Industrial Internet of Things
Javaid, U., & Sikdar, B. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3032607 · Cited by 44
Smart contract and IPFS-based trustworthy secure data storage and device authentication scheme in fog computing environment
Dwivedi, S. K., Amin, R., & Vollala, S. (2022) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-022-01376-7 · Cited by 41
A Survey on Blockchain-Based Federated Learning
Wu, L., Ruan, W., Hu, J., & He, Y. (2023) · Future Internet · doi:10.3390/fi15120400 · Cited by 48
Security and privacy of industrial big data: Motivation, opportunities, and challenges
Anjum, N., Latif, Z., & Chen, H. (2025) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2025.104130 · Cited by 41
A Survey on Blockchain-Based Federated Learning and Data Privacy
Chhetri, B., Gopali, S., Olapojoye, R., Sani, S. D., & Namin, A. S. (2023) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac57700.2023.00199 · Cited by 42
Current Research Trends in IoT Security: A Systematic Mapping Study
Lee, J. Y., & Lee, J. (2021) · Mobile Information Systems · doi:10.1155/2021/8847099 · Cited by 42
PCNNCEC: Efficient and Privacy-Preserving Convolutional Neural Network Inference Based on Cloud-Edge-Client Collaboration
Wang, J., He, D., Castiglione, A., Gupta, B. B., Karuppiah, M., & Wu, L. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3177755 · Cited by 38
Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability
Frankó, A., Hollósi, G., Ficzere, D., & Varga, P. (2022) · Sensors · doi:10.3390/s22239148 · Cited by 52
Adversarial Machine Learning in Industry: A Systematic Literature Review
Jedrzejewski, F. V., Thode, L., Fischbach, J., Gorschek, T., Méndez, D., & Lavesson, N. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103988 · Cited by 44
Federated learning for anomaly detection on Internet of Medical Things: A survey
Pinto, R. P., Silva, B. M. C., & Inácio, P. R. M. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101677 · Cited by 41
Appositeness of Optimized and Reliable Machine Learning for Healthcare: A Survey
Swain, S., Bhushan, B., Dhiman, G., & Viriyasitavat, W. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09733-8 · Cited by 40
A Review of Blockchain Technology in Knowledge-Defined Networking, Its Application, Benefits, and Challenges
Wijesekara, P. A. D. S. N., & Gunawardena, S. (2023) · Network · doi:10.3390/network3030017 · Cited by 48
Exploiting Blockchain to Make AI Trustworthy: A Software Development Lifecycle View
Zhang, P., Ding, S., & Zhao, Q. (2023) · ACM Computing Surveys · doi:10.1145/3614424 · Cited by 46
A Hybrid Approach With GAN and DP for Privacy Preservation of IIoT Data
Hindistan, Y. S., & Yetkin, E. F. (2023) · IEEE Access · doi:10.1109/access.2023.3235969 · Cited by 36
Differential privacy model for blockchain based smart home architecture
Qashlan, A., Nanda, P., & Mohanty, M. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.08.010 · Cited by 36
Blockchain and Edge Computing for Decentralized EMRs Sharing in Federated Healthcare
Nguyen, D. C., Pathirana, P. N., Ding, M., & Seneviratne, A. P. (2020) · Globecom · doi:10.1109/globecom42002.2020.9347951 · Cited by 35
PCFed: Privacy-Enhanced and Communication-Efficient Federated Learning for Industrial IoTs
Han, Q., Yang, S., Ren, X., Zhao, P. F., Zhao, C., & Wang, Y. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3161673 · Cited by 37
A Blockchain-Assisted Trusted Federated Learning for Smart Agriculture
Manoj, T., Makkithaya, K., & Narendra, V. G. (2025) · SN Computer Science · doi:10.1007/s42979-025-03672-4 · Cited by 47
Transportation 4.0 in supply chain management: State-of-the-art and future directions towards 5.0 in the transportation sector
Wong, W. P., Anwar, M. F., & Soh, K. L. (2024) · Operations Management Research · doi:10.1007/s12063-024-00471-7 · Cited by 30
Federated Learning for Human Activity Recognition: Overview, Advances, and Challenges
Aouedi, O., Sacco, A., Khan, L. U., Nguyen, D. C., & Guizani, M. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3484228 · Cited by 42
HOPCTP: A Robust Channel Categorization Data Preservation Scheme for Industrial Healthcare Internet of Things
Adil, M., Attique, M., Khan, M. M., Ali, J., Farouk, A., & Song, H. H. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3148287 · Cited by 30
Privacy-preserving location data stream clustering on mobile edge computing and cloud
Stephanie, V., Chamikara, M. A. P., Khalil, I., & Atiquzzaman, M. (2021) · Information Systems · doi:10.1016/j.is.2021.101728 · Cited by 29
Self-citation
A neutrosophic WPM-based machine learning model for device trust in industrial internet of things
Khan, M. A., & Alghamdi, N. S. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03431-2 · Cited by 29
Decentralized Federated Learning for Industrial IoT With Deep Echo State Networks
Qiu, W., Ai, W., Chen, H., Feng, Q., & Tang, G. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3194627 · Cited by 32
RL-Based Federated Learning Framework Over Blockchain (RL-FL-BC)
Riahi, A. A., Mohamed, A. M. S., & Erbad, A. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3241437 · Cited by 28
Fair Outsourcing Polynomial Computation Based on the Blockchain
Guan, Y., Zheng, H., Shao, J., Lu, R., & Wei, G. (2021) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2021.3054772 · Cited by 28
Boosting Accuracy of Differentially Private Federated Learning in Industrial IoT With Sparse Responses
Cui, L., Ma, J., Zhou, Y., & Yu, S. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3161517 · Cited by 28
Deep Learning-Based Privacy-Preserving Data Transmission Scheme for Clustered IIoT Environment
Lakshmanna, K., Kavitha, R., Geetha, B. T., Nanda, A. K., Radhakrishnan, A., & Kohar, R. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/8927830 · Cited by 28
On the use of artificial intelligence to deal with privacy in IoT systems: A systematic literature review
Giordano, G., Palomba, F., & Ferrucci, F. (2022) · Journal of Systems and Software · doi:10.1016/j.jss.2022.111475 · Cited by 28
Revolutionizing healthcare organizations with Operational Excellence and Healthcare 4.0: a systematic review of the state-of-the-art literature
Detwal, P. K., Agrawal, R., Samadhiya, A., Kumar, A., V, & Garza‐Reyes, J. A. (2023) · International Journal of Lean Six Sigma · doi:10.1108/ijlss-04-2023-0061 · Cited by 27
CFSL: A Credible Federated Self-Learning Framework
Zhang, W., Bao, Z., Liu, Y., Xu, L. J., Lu, Q., Ning, H., Wang, X., Yang, S., Wang, F., & Li, Z. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3286398 · Cited by 27
Gradient leakage attacks in federated learning
Gong, H., Jiang, L., Liu, X., Wang, Y., Gastro, O., Wang, L., Zhang, K., & Guo, Z. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10550-z · Cited by 30
Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach
Mishra, K. N., Bhattacharjee, V., Saket, S., & Mishra, S. P. (2022) · Cluster Computing · doi:10.1007/s10586-022-03813-x · Cited by 25
DetectPMFL: Privacy-Preserving Momentum Federated Learning Considering Unreliable Industrial Agents
Zhang, Z., He, N., Li, Q., Wang, K., Gao, H., & Gao, T. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3140806 · Cited by 24
Perturbation-enabled Deep Federated Learning for Preserving Internet of Things-based Social Networks
Salim, S., Moustafa, N., Turnbull, B., & Razzak, I. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3537899 · Cited by 24

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

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

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks