Privacy-Aware Blockchain Approach for Efficient and Secure E-Health Applications

The integration of the Internet of Things (IoT) in E-healthcare has enabled intelligent heart attack detection (HAD) leveraging artificial intelligence (AI). However, existing approaches face high computational complexity, limited accuracy, and a lack of secure bidirectional communication between patients and healthcare providers (HPs) while preserving patients' privacy. While homomorphic encryption and secure multi-party computation can protect privacy, they incur substantial overhead. To overcome these limitations, we propose a novel lightweight HAD methodology that ensures the preservation of security and privacy while maintaining performance reliability. First, we introduce a customized consortium blockchain harnessing group signatures to ensure patient anonymity and data unlinkability, enabling patients to securely communicate with HPs through unique and untraceable identifiers. Next, we develop a lightweight HAD model via knowledge distillation (KD) from a complex high-performing model that captures spatial-temporal patterns and emphasizes key features in health data. Then, we devised a functional encryption (FE)-based cryptosystem to protect patients' data during model execution. Lastly, we advance explainable AI (XAI) to highlight influential health features and enhance clinical trust. Evaluated on a real-world heart dataset and deployed on a real testbed, our HAD model achieves 99.22% accuracy and a 99.23% F1-score, outperforming state-of-the-art techniques while reducing memory footprint, parameter count, and inference time by 98.19%, 98.58%, and 60.18%, respectively, and fostering interpretability and trust in clinical decision-making. Furthermore, the proposed blockchain with the FE approach enables secure model execution, protects patient data, and scales efficiently to 500,000 patients in under 2.5 minutes, while minimizing communication and computational overhead, achieving 95.43% and 89.06% reductions, respectively.