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<p>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 (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\Pr [\text {eavesdrop}] \le 2^{-8168}\)</EquationSource> </InlineEquation>), though constrained to static or low-mobility scenarios due to free-space optical requirements; (ii) lightweight homomorphic encryption (LightHE) over <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\mathbb {Z}_{2^{40}}[x]/(x^{4096}+1)\)</EquationSource> </InlineEquation> enabling exact integer arithmetic with <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\mathcal {O}(d \log d)\)</EquationSource> </InlineEquation> complexity; (iii) Hierarchical Federated Learning with Krum-based Byzantine resilience against <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(f/N = 1/3\)</EquationSource> </InlineEquation> 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 <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(N=1500\)</EquationSource> </InlineEquation> sensors across 200 virtual patients, QEPA demonstrates near-optimal utility, achieving 96.8% diagnostic accuracy (only 0.3% below FedAvg) with NRMSE = <InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(9.7 \times 10^{-3}\)</EquationSource> </InlineEquation>, while providing unprecedented efficiency: 8.05<InlineEquation ID="IEq7"> <EquationSource Format="TEX">\(\times\)</EquationSource> </InlineEquation> lower latency (31.8 ms) and 5.96<InlineEquation ID="IEq8"> <EquationSource Format="TEX">\(\times\)</EquationSource> </InlineEquation> lower energy (3.32 mJ) versus Paillier, alongside 17.1<InlineEquation ID="IEq9"> <EquationSource Format="TEX">\(\times\)</EquationSource> </InlineEquation> communication compression (7.68 kbit). Critically, QEPA reduces membership inference risk to 1.5% (vs. 28.3% in FedAvg) while maintaining <InlineEquation ID="IEq10"> <EquationSource Format="TEX">\(\epsilon \le 0.01\)</EquationSource> </InlineEquation> differential privacy and resisting quantum attacks with <InlineEquation ID="IEq11"> <EquationSource Format="TEX">\(&gt;2^{400}\)</EquationSource> </InlineEquation>-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.</p>

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Quantum-enhanced privacy aggregation for healthcare monitoring in wireless body area networks

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  • 发表日期：

    2026年3月17日

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  • Soufiane Ben Othman,
  • Obaid Ali

摘要

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 ( \(\Pr [\text {eavesdrop}] \le 2^{-8168}\) ), though constrained to static or low-mobility scenarios due to free-space optical requirements; (ii) lightweight homomorphic encryption (LightHE) over \(\mathbb {Z}_{2^{40}}[x]/(x^{4096}+1)\) enabling exact integer arithmetic with \(\mathcal {O}(d \log d)\) complexity; (iii) Hierarchical Federated Learning with Krum-based Byzantine resilience against \(f/N = 1/3\) 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 \(N=1500\) sensors across 200 virtual patients, QEPA demonstrates near-optimal utility, achieving 96.8% diagnostic accuracy (only 0.3% below FedAvg) with NRMSE = \(9.7 \times 10^{-3}\) , while providing unprecedented efficiency: 8.05 \(\times\) lower latency (31.8 ms) and 5.96 \(\times\) lower energy (3.32 mJ) versus Paillier, alongside 17.1 \(\times\) communication compression (7.68 kbit). Critically, QEPA reduces membership inference risk to 1.5% (vs. 28.3% in FedAvg) while maintaining \(\epsilon \le 0.01\) differential privacy and resisting quantum attacks with \(>2^{400}\) -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.