Blockchain-based Quantum-Resilient Privacy Models for Medical IoT Systems
The rapid expansion of the Internet of Medical Things (IoMT) has revolutionized healthcare data collection, transmission, and analysis. However, the increasing sophistication of cyberattacks—especially the looming threat of quantum computing, poses significant challenges to maintaining data confidentiality, integrity, and privacy. This study proposes a Blockchain-based Quantum-Resilient Privacy Model that integrates lattice-based post-quantum cryptography (PQC) with homomorphic encryption and a Convolutional Neural Network (PQC-CNN) for secure and privacy-preserving analysis of encrypted electrocardiogram (ECG) data. The proposed framework employs blockchain to ensure immutable, auditable data storage and leverages PQC to safeguard against both classical and quantum attacks. Experimental results demonstrate that the PQC-CNN achieved superior classification performance, with an accuracy of 0.95, precision of 0.96, recall of 0.95, and F1-score of 0.955, outperforming traditional models such as SVM, RF, and k-NN. Privacy-preserving computations,including statistical aggregation, anomaly detection, and trend analysis—were successfully performed directly on encrypted ECG data using lattice-based homomorphic encryption, achieving full privacy preservation with minimal computational overhead. Blockchain evaluation results revealed high throughput (up to 1050 Tx/sec) and efficient block storage (up to 95%), confirming the system’s scalability for IoMT environments. The combination of blockchain immutability, post-quantum encryption, and privacy-preserving analytics ensures a robust, quantum-safe infrastructure for secure healthcare data management. This work contributes a novel, scalable, and quantum-resilient architecture capable of protecting sensitive medical data in next-generation IoMT systems.
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