Cloud-IIoT-Based Electronic Health Record Privacy-Preserving by CNN and Blockchain-Enabled Federated Learning

• Published in: IEEE transactions on industrial informatics 2023-01, Vol.19 (1), p.1080-1087
• Main Authors: Alzubi, Jafar A., Alzubi, Omar A., Singh, Ashish, Ramachandran, Manikandan
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Blockchain; Blockchain-enabled federated learning; Blockchains; Cloud computing; cloud-Industrial Internet of Things (IIoT); Collaborative work; convolutional neural network (CNN); Convolutional neural networks; Cryptography; Data models; Data privacy; Deep learning; Electronic health records; Federated learning; Health care; healthcare industry; Internet of Things; Machine learning; Medical services; Privacy; privacy preservation; Security
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2022.3189170

Industrial cloud computing and Internet of Things have transformed the healthcare industry with the rapid growth of distributed healthcare data. Security and privacy of healthcare data are crucial challenges in the healthcare industry. This article proposes a novel technique using deep learning and blockchain techniques for electronic health record privacy-preservation. The processed dataset classified normal and abnormal users using the convolutional neural network approach. Then, by using blockchain integrated with a cryptography-based federated learning module, the abnormal users have been processed and removed from the database along with the accessibility for the health records. The simulation has been done in the Python tool and experimental results show that the model's classification results and performance are better than other existing techniques.