Explainable AI With Homomorphic Encryption for Secure Cloud-Based ECG Analysis in Heart Disease Diagnosis

Electrocardiogram (ECG) analysis is widely used for early detection of cardiac abnormalities, yet the deployment of deep learning models in cloud environments raises concerns regarding data privacy and clinical interpretability. To address these challenges, this work presents a novel framework that integrates homomorphic encryption with explainable deep learning for secure and interpretable ECG classification in the cloud. The novel framework that integrates homomorphic encryption with explainable deep learning for secure ECG-based heart disease diagnosis in the cloud. Explainable AI (XAI) was employed to enhance clinician and patient trust, while homomorphic encryption (HE) ensures confidentiality of sensitive ECG signals during cloud-based processing. The originality of this lies in jointly addressing three critical requirements—data privacy, interpretability, and computational efficiency—within a single diagnostic pipeline. The proposed method employs a convolutional neural network (CNN) optimized for encrypted computation and applies SHapley Additive exPlanations (SHAP) to provide interpretable results aligned with clinical decision-making. Experimental validation on the MIT-BIH dataset demonstrates that the model achieves 94.2% accuracy in plaintext inference and 93.4% under encrypted inference, with an F1-score of 92.0% and 91% agreement with cardiologists, while maintaining an average encrypted inference latency of 420 ms, demonstrating its practicality for secure cloud deployment. These results confirm that the framework offers a practical and trustworthy solution for secure, cloud-based ECG diagnostics.