K‐Anonymization and Residual Neuron Attention Network for Privacy Data Protection in Blockchain Network With Federated Learning Using Defense Application

K‐Anonymization and Residual Neuron Attention Network for Privacy Data Protection in Blockchain Network With Federated Learning Using Defense Application T. Premkumar Department of Computer Applications PG, Vels Institute of Science Technology and Advanced Studies Chennai India https://orcid.org/0009-0004-1818-9689 D. R. Krithika Department of Computer Applications PG, Vels Institute of Science Technology and Advanced Studies Chennai India ABSTRACT

Blockchain acts as an important potential in the defense applications for several defense uses because of its features, namely transparency, decentralization, immutability, and security. However, protecting privacy data has various security liabilities and attack issues. Therefore, a new model named Residual Neuron Attention Network (ResNA‐Net) has been devised for privacy data protection in defense applications. In Federated Learning (FL), the entities, like server and nodes are included. Here, local training is done and the weights are updated to the server first, and next, model aggregation at the server is executed. Then, the global model is downloaded at all nodes, training is updated, and the process is iterated at all epochs. Meanwhile, in local training, the input defense data is normalized by Min‐max normalization and then augmented using oversampling. Then, k‐anonymization is executed using Fractional Gradient Beluga Whale Optimization (FGBWO). Next, privacy‐protected data classification is executed by employing ResNA‐Net, which is engineered by the combination of Deep Residual Network (DRN) and Neuron Attention Stage‐by‐Stage Net (NasNet). The ResNA‐Net achieved high performance and the immutable nature of the blockchain used in the ResNA‐Net model protects the defense data during the entire process of the system. The hybrid ResNA‐Net effectively learns the complex features and this capability improves the accuracy of the model. The high‐performance results obtained by the devised model highly protect sensitive data thereby providing security and privacy in defense data applications.
06 09 2025 06 2025 e70182 10.1002/ett.70182 2 10.1002/crossmark_policy onlinelibrary.wiley.com true 2024-08-02 2025-05-08 2025-06-09 http://onlinelibrary.wiley.com/termsAndConditions#vor 10.1002/ett.70182 https://onlinelibrary.wiley.com/doi/10.1002/ett.70182 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ett.70182 Artificial Intelligence and Statistics McMahan B. 1273 2017 10.1561/2200000083 10.1007/978-3-030-32692-0_16 10.1038/s41746-020-00323-1 10.1109/TR.2020.2996261 R.SuvarnaandA. M.Kowshalya “Credit Card Fraud Detection Using Federated Learning Techniques ”2020. 10.1109/BDAI.2018.8546629 National Defence Studies Institute Journal Changsan U. 55 14 1 2023 A Survey of Privacy‐Enhancing Technology: Case Study for Military Vehicle 10.1016/j.bcra.2023.100135 10.23919/JCC.2020.09.002 10.1109/TII.2021.3085960 Secure Blockchain Model for Vehicles toll Collection by GPS Tracking: A Case Study of India Sahoo S. S. 1 2022 10.1007/s11042-023-15634-0 10.1155/2021/7705843 10.1109/ICUFN49451.2021.9528593 10.1109/TII.2022.3168011 A.Yazdinejad A.Dehghantanha R. M.Parizi M.Hammoudeh H.Karimipour andG.Srivastava “Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain‐Based Iiot Networks ”arXiv preprint arXiv:2204.09829 2022. 10.1109/ICBASE51474.2020.00033 10.1109/TII.2017.2773646 International Summit Smart City 360° Zapoglou N. 386 2020 10.1109/JIOT.2020.3015772 Expert Systems Javed L. 1 40 5 2022 ShareChain: Blockchain‐Enabled Model for Sharing Patient Data Using Federated Learning and Differential Privacy 10.1155/2021/4376418 10.1007/978-3-031-08223-8_23 10.47893/IJCCT.2013.1201 10.1155/2014/396529 S.Ruder “An Overview of Gradient Descent Optimization Algorithms ”arXiv preprint arXiv:1609.04747 2016. 10.1016/j.knosys.2022.109215 10.1016/j.enconman.2019.111793 X.QinandZ.Wang “Nasnet: A Neuron Attention Stage‐By‐Stage Net for Single Image Detraining ”arXiv preprint arXiv:1912.03151 2019. 10.1109/JIOT.2020.3030072 10.1145/3460427 Proceedings of 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET) Madan S. 1 2018 “Global Armed Forces Dataset ”accessed April 2024 https://www.kaggle.com/datasets/abhijitdahatonde/global‐armed‐forces‐dataset. “The CNS North Korea Missile Test Database ”accessed April 2024 https://data.world/ian/the‐cns‐north‐korea‐missile‐test‐database. “Nuclear Weapon Explosions ”accessed April 2024 https://data.world/tdreabing/nuclear‐weapon‐explosions. “2022 Russia Ukraine War Dataset ”accessed October 2024 https://www.kaggle.com/datasets/piterfm/2022‐ukraine‐russian‐war. 10.1109/ACCESS.2023.3345360 10.1080/09540091.2023.2199951