The rapid proliferation of wireless body area networks (WBANs) has intensified the need for architectures that simultaneously guarantee quantum-resistant security, patient privacy, and clinical-grade diagnostic fidelity. This paper introduces quantum-enhanced privacy aggregation (QEPA), a framework that establishes a new state-of-the-art in secure IoMT by fulfilling the “Triple Constraint” of Privacy, Robustness, and Diagnostic Fidelity, albeit with a critical dependency on line-of-sight quantum channels. QEPA’s defense-in-depth architecture integrates four synergistic innovations: (i) BB84 Quantum Key Distribution providing information-theoretic security ([Formula: see text]), though constrained to static or low-mobility scenarios due to free-space optical requirements; (ii) lightweight homomorphic encryption (LightHE) over [Formula: see text] enabling exact integer arithmetic with [Formula: see text] complexity; (iii) Hierarchical Federated Learning with Krum-based Byzantine resilience against [Formula: see text] compromised nodes; and (iv) SHAP-based explainable AI achieving 97.8% attribution fidelity against cardiologist-validated ground truth. Evaluated on a large-scale WBAN with [Formula: see text] sensors across 200 virtual patients, QEPA demonstrates near-optimal utility, achieving 96.8% diagnostic accuracy (only 0.3% below FedAvg) with NRMSE = [Formula: see text], while providing unprecedented efficiency: 8.05× lower latency (31.8 ms) and 5.96× lower energy (3.32 mJ) versus Paillier, alongside 17.1× communication compression (7.68 kbit). Critically, QEPA reduces membership inference risk to 1.5% (vs. 28.3% in FedAvg) while maintaining [Formula: see text] differential privacy and resisting quantum attacks with [Formula: see text]-operation hardness. While the current implementation’s reliance on line-of-sight QKD limits deployment in high-mobility clinical environments, these results establish QEPA as quantum-safe framework suitable for FDA-cleared life-critical medical telemetry in controlled settings, with clear pathways toward non-line-of-sight quantum networking.
The rapid proliferation of wireless body area networks (WBANs) has intensified the need for architectures that simultaneously guarantee quantum-resistant security, patient privacy, and clinical-grade diagnostic fidelity. This paper introduces quantum-enhanced privacy aggregation (QEPA), a framework that establishes a new state-of-the-art in secure IoMT by fulfilling the “Triple Constraint” of Privacy, Robustness, and Diagnostic Fidelity, albeit with a critical dependency on line-of-sight quantum channels. QEPA’s defense-in-depth architecture integrates four synergistic innovations: (i) BB84 Quantum Key Distribution providing information-theoretic security ([Formula: see text]), though constrained to static or low-mobility scenarios due to free-space optical requirements; (ii) lightweight homomorphic encryption (LightHE) over [Formula: see text] enabling exact integer arithmetic with [Formula: see text] complexity; (iii) Hierarchical Federated Learning with Krum-based Byzantine resilience against [Formula: see text] compromised nodes; and (iv) SHAP-based explainable AI achieving 97.8% attribution fidelity against cardiologist-validated ground truth. Evaluated on a large-scale WBAN with [Formula: see text] sensors across 200 virtual patients, QEPA demonstrates near-optimal utility, achieving 96.8% diagnostic accuracy (only 0.3% below FedAvg) with NRMSE = [Formula: see text], while providing unprecedented efficiency: 8.05× lower latency (31.8 ms) and 5.96× lower energy (3.32 mJ) versus Paillier, alongside 17.1× communication compression (7.68 kbit). Critically, QEPA reduces membership inference risk to 1.5% (vs. 28.3% in FedAvg) while maintaining [Formula: see text] differential privacy and resisting quantum attacks with [Formula: see text]-operation hardness. While the current implementation’s reliance on line-of-sight QKD limits deployment in high-mobility clinical environments, these results establish QEPA as quantum-safe framework suitable for FDA-cleared life-critical medical telemetry in controlled settings, with clear pathways toward non-line-of-sight quantum networking.
The development of the application of making use of Wireless Body Area Networks (WBANs) in medically created systems has presented fresh security concerns, in particular with the advent of quantum computing threatening the conventional cryptographic ideas. To proposes a new model Scalable Quantum-Intelligent Blockchain Framework (SQIBF) and a Deep Quantum Neural Architecture (DQNA) to privacy-preserving healthcare communications. The proposed framework is a synergistic approach of post-quantum lattice-based cryptography, in combination with quantum key protocols distribution schemes with federated quantum machine learning to develop multi-layered security infrastructure of the body sensor networks. We introduce Quantum Enhanced Consensus Mechanism (QECM), which deploys quantum random number generation to deliver Byzantine fault tolerance, and which can withstand symptoms of quantum-enabled parties by 97.5. The DQNA implements variational quantum gate scale used to detect textbook anomalies and categorize confidential medical data variational quantum circuit with a parameterized scale according to parameters to support the 94.3 percent mark on benchmark medical datasets with a differential privacy clarification. Experimental Assessment Simulations on simulated quantum systems and experimental WBAN testbeds have shown that it, SQIBF, can decrease the key establishing latency by approximately 40 percent when compared to classical blockchain methods using resource optimizing biosensors resource exploratory consumptions. Its model ensures the compliance of the HIPAA and GDPR requirements and homomorphic encryption layers with zero-knowledge proofs. Scalability of performance benchmarking implies up to 10,000 active sensor nodes and sub-millisecond finality of transaction. The present work introduces an end-to-end quantum resistant system to facilitate the muchneeded transitional process of quantum computing, distributed ledger technology, and health care IoT and establishes a platform of safe telemedicine infrastructure in the next generation.
We introduce QEDRA (Quantum-Entangled Decentralized Reasoning and Aggregation), the first practical framework that simultaneously addresses ultra-scarce data regimes ( nk<20 ), quantum-scale security threats, and ethical requirements in healthcare AI. Unlike conventional federated learning systems that sacrifice privacy for utility, QEDRA leverages 9-qubit W-state entanglement ( ∣W9⟩=91∑i=19∣1i⟩ ) to generate semantically aware noise that improves utility while providing formal (ϵ,δ) -differential privacy guarantees ( ϵ=0.08 –0.17, δ=10−16 ). QEDRA’s NISQ-compatible architecture integrates quantum physics with neuro-symbolic reasoning through four key innovations: (1) Quantum-Entangled Differential Privacy (QEDP) that breaks the classical privacy-utility trade-off; (2) Neuro-Symbolic Swarm Intelligence with Quantum-Entangled PSO (QEPSO) for multimodal fusion; (3) Privacy-Aware LLM Fine-Tuning with symbolic guardrails; and (4) Verifiable Ethical Governance via Quantum Multi-Party Computation. Validated on MIMIC-IV, QEDRA achieves 87.0% F1-score (45% higher than FedAvg), 0.4% attack success rate, and 99% fault recovery under decoherence ( pdep=0.005 ), all while maintaining Tround<2.4 s latency through BB84-QKD-secured channels. Critically, QEDRA is engineered for real-world deployment on IBM 127-qubit Eagle processors ( QV=128 ) with seamless classical fallback when entanglement fidelity drops below F<0.98 . By co-designing quantum circuits with classical resilience, QEDRA delivers provable trustworthiness without performance compromise, establishing a new standard for quantum-resilient, ethically grounded AI in resource-constrained medical environments.
AI-driven medical diagnostics increasingly requires collaborative model training across institutions, yet centralizing patient data conflicts with privacy regulations. Federated Learning enables distributed training without raw data sharing, but remains vulnerable to gradient inversion and model leakage attacks. Furthermore, harvest-now-decrypt-later attacks render computationally secure protocols insufficient for protecting long-lived medical records. Quantum communication offers information-theoretic security immune to such threats, making Quantum Federated Learning (QFL) a compelling framework for healthcare. However, practical deployment is constrained by communication overhead and quantum channel noise. We present a systematic quantitative study of communication, convergence, and noise trade-offs in QFL, introducing two complementary strategies to reduce quantum transmissions: (1) structured parameter reduction via light-cone feature selection in parameterized quantum circuits, and (2) a Hybrid QFL architecture that dynamically switches between centralized and decentralized aggregation. We show that Hybrid QFL reduces total quantum transmissions from 3TNMP, the cost of pure Centralized QFL, to {3t+2(T−t)}NMP over T rounds while preserving near-centralized convergence. We further demonstrate that decentralized aggregation is more noise-resilient under depolarizing noise, and evaluate Steane code-based quantum error correction in high-noise regimes. Our results provide an integrated design framework for communication-efficient, noise-aware QFL, clarifying practical trade-offs for scalable quantum-secure distributed learning in healthcare.
Objective: The authors propose and evaluate a novel cybersecurity architecture for telehealth that is resilient against future quantum computing cyber threats. By integrating post-quantum cryptography (PQC) with quantum key distribution (QKD) and privacy-preserving mechanisms, data confidentiality and immutability for patient records in a post-quantum era are ensured. Methods: A multi-layered design approach was adopted. The PQC algorithms (e.g. CRYSTALS-Dilithium) were integrated at the blockchain consensus layer to resist quantum attacks. A directed acyclic graph (DAG)-based ledger managed high transaction throughput and latency constraints typical of telehealth. A QKD-enhanced key management protocol leveraged quantum channels for secure exchanges. Zero-knowledge proofs (ZKPs) and secure multiparty computation (MPC) verified transactions without exposing sensitive patient data. A granular access control model used attribute-based encryption and smart contracts to govern which participants could view or modify encrypted medical records. Results: The prototype was developed within a simulated telehealth network comprising hospitals, clinics, and patient devices. The PQC signatures at the consensus layer provided effective resistance to both classical and anticipated quantum attacks. The QKD facilitated secure key distribution, while ZKPs and MPC enabled validation of healthcare transactions without compromising patient privacy. Despite increased computational overhead, the DAG approach efficiently handled parallel transactions, indicating improved scalability compared to traditional linear blockchains. Conclusion: A QKD-enhanced, PQC-driven framework successfully addresses critical security and privacy requirements, safeguarding medical data from emerging quantum threats. Although overhead and infrastructural costs are significant, sustained cryptographic resilience and robust patient confidentiality underscore its suitability for next-generation healthcare systems. Future studies should explore additional optimizations, homomorphic encryption, and larger-scale pilots under regulatory standards.
ABSTRACT Objective To design a secure Federated Learning (FL) framework for Internet of Medical Things (IoMT) that protects sensitive patient data from both classical and quantum attacks. Methods Proposed the QSFedMA‐IoMT protocol integrating quantum and classical security techniques. Utilized entanglement‐based E91 protocol for generating a highly secure root key to establish trust. Applied BB84 protocol for efficient generation of per‐round session keys during FL updates. Incorporated classical cryptographic scheme AES‐GCM for secure communication. Employed privacy‐enhancing techniques such as norm‐clipping and Gaussian noise to mitigate information leakage during model training. Results Our work demonstrates robust resistance against both classical and quantum adversaries, while enhancing data privacy through secure key distribution and differential privacy mechanisms. It ensures the integrity of model updates within the federated learning process and achieves an effective balance between strong security guarantees and computational efficiency, making it well‐suited for IoMT environments. Conclusion The QSFedMA‐IoMT protocol delivers a robust and practical hybrid framework for securing federated learning in healthcare systems. By integrating E91 and BB84 protocols, it strengthens key management and trust establishment. The combination of quantum security with classical privacy‐preserving techniques ensures resilience, scalability, and efficiency. Overall, this work provides a promising direction for secure and privacy‐aware federated learning in next‐generation IoMT applications.
The rapid transformation of defense communication ecosystems into distributed, AI-enabled, and multi-domain operational environments has exposed significant vulnerabilities in existing cryptographic infrastructures. These vulnerabilities are particularly critical in light of the imminent advancement of quantum computing, which threatens conventional public-key encryption schemes, and the operational limitations imposed by lightweight encryption mechanisms deployed on resource-constrained battlefield devices. The traditional public-key algorithm like RSA and ECC, could be exploited by quantum attacks, and the resource-efficient ciphers are not always effective to protect against advanced cyber-warfare, interception, and integrity-compromise in high-mobility tactical networks. This paper suggests DEF-CRYPT-Q (Defense Cryptographic Quantum-Enhanced Privacy-Preserving Hybrid Framework) to secure distributed defense communication data because it is motivated by the requirement of having a single, quantum-resilient, and privacy-preserving security architecture that is suitable in the context of heterogeneous defense settings. The suggested architecture will have four synergistic elements, namely (i) a Context-Aware Lightweight Defense Encryption (CALDE) module that is optimized to support constrained soldier wearables, UAV nodes, and edge sensors; (ii) a Quantum-Resistant Cryptographic Layer (QRCL) that uses lattice-based post-quantum primitives, such as CRYSTALS-Kyber to support the encapsulation of secure keys and CRYSTALS-Dilithium to support the generation of digital signatures, providing long-term quantum safety The experimental assessment of a heterogeneous model of a defense communication demonstrates the shortening of the encryption latency, the limited computational costs of the models of constrained platforms, the high level of integrity, and the resistance to classical and quantum adversarial models, which in turn makes it possible to claim that DEF-CRYPT-Q is a scalable and prospective cryptographic paradigm that is consistent with the strategic goals of modernizing its defense. Simulation results demonstrate that Adaptive DDP-QKA reduces encryption latency to 14.83 ms (vs. 18.21 ms classical and 21.48 ms naïve PQC) and bandwidth overhead to 13.9% (vs. 124.6% PQC), while maintaining moderate energy use (1.14 units). It achieves high quantum resistance (0.92 score), mission adaptability (0.95), and low residual risk (< 0.1 for major threats), verifying appropriateness for latency-sensitive defense communications.