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

TLDR

Industrial cloud computing and IoT have transformed healthcare with rapid growth of distributed data, yet security and privacy remain critical challenges. The study proposes a novel technique that combines deep learning and blockchain to preserve the privacy of electronic health records. The method employs a convolutional neural network to classify users, then uses blockchain‑enabled cryptographic federated learning to remove abnormal users and restrict database access. Simulation results demonstrate that the model outperforms existing techniques in classification accuracy and overall performance.

Abstract

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.