Quantum-Resistant Lightweight Post-Quantum Distributed Ledger Security Framework with Deep Learning-Based Storage Optimization for Healthcare Systems

Abstract

The rapid evolution of quantum computing poses severe threats to classical cryptographic primitives employed in distributed ledger systems, which are vulnerable to quantum algorithms like Shor’s algorithm. This study proposes a Quantum-Resistant lightweight distributed ledger security framework integrating Post-quantum Cryptography (QR-PQ), lightweight encryption, and deep learning-based storage optimization to ensure sustainable security and scalability in resource-constrained environments. The framework employs lattice-based and hash-based PQC algorithms for robust encryption and digital signatures, ensuring long-term quantum resilience. Lightweight cryptographic modules minimize computational complexity for real-time IoT and healthcare applications. A deep learning-driven pruning and compression mechanism dynamically optimizes distributed ledger storage, retaining critical data while removing redundant records without compromising integrity. Experimental evaluation on a medical distributed ledger dataset of 50,000 Electronic Health Records demonstrates significant reductions in hash computation time (56%), encryption/decryption latency (66%), and storage overhead (up to 60%) compared to traditional distributed ledger models. Results affirm that the proposed QR-LPQCF achieves quantum resistance, computational efficiency, and scalable storage, making it highly suitable for secure data handling in future quantum-augmented environments. This framework establishes a foundational paradigm for designing quantum-secure and storage-optimized distributed ledger architectures for next-generation digital infrastructures.