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<p>Wireless Body Area Networks (WBANs) empower continuous health monitoring but face formidable challenges in safeguarding sensitive medical data under severe resource constraints and looming quantum threats. We introduce PPDA-NEGA-PQHE, a revolutionary framework for privacy-preserving data aggregation in WBANs, integrating lattice-based post-quantum homomorphic encryption (PQHE), a neuro-evolutionary genetic algorithm (NEGA), an immune-inspired trust model, game-theoretic adversarial defense, and bio-inspired self-healing mechanisms. PPDA-NEGA-PQHE tackles a multi-objective optimization problem, harmonizing security, energy efficiency, latency, accuracy, and resilience, with NEGA neural-inspired adaptability ensuring robust performance amidst patient mobility and node failures. The immune trust model precisely identifies malicious nodes, game-theoretic strategies neutralize colluding attacks, and self-healing trees bolster reliability. Rigorous evaluation on real-world medical datasets reveals unparalleled performance: 40 ms latency, 0.8 mW power consumption, and 0.8% aggregation error, surpassing HCEL, EEMR, and state-of-the-art protocols by 30–60%. Statistical validation confirms its superiority, establishing PPDA-NEGA-PQHE as a scalable, quantum-resistant cornerstone for next-generation healthcare IoT, redefining secure medical monitoring.</p>

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Privacy-preserving data aggregation in WBNAs using neuro-evolutionary algorithms and post-quantum homomorphic encryption

  • 期刊论文

  • 发表日期：

    2025年10月15日

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  • Soufiane Ben Othman

摘要

Wireless Body Area Networks (WBANs) empower continuous health monitoring but face formidable challenges in safeguarding sensitive medical data under severe resource constraints and looming quantum threats. We introduce PPDA-NEGA-PQHE, a revolutionary framework for privacy-preserving data aggregation in WBANs, integrating lattice-based post-quantum homomorphic encryption (PQHE), a neuro-evolutionary genetic algorithm (NEGA), an immune-inspired trust model, game-theoretic adversarial defense, and bio-inspired self-healing mechanisms. PPDA-NEGA-PQHE tackles a multi-objective optimization problem, harmonizing security, energy efficiency, latency, accuracy, and resilience, with NEGA neural-inspired adaptability ensuring robust performance amidst patient mobility and node failures. The immune trust model precisely identifies malicious nodes, game-theoretic strategies neutralize colluding attacks, and self-healing trees bolster reliability. Rigorous evaluation on real-world medical datasets reveals unparalleled performance: 40 ms latency, 0.8 mW power consumption, and 0.8% aggregation error, surpassing HCEL, EEMR, and state-of-the-art protocols by 30–60%. Statistical validation confirms its superiority, establishing PPDA-NEGA-PQHE as a scalable, quantum-resistant cornerstone for next-generation healthcare IoT, redefining secure medical monitoring.