Quantum Agentic Secure Communication for Adaptive and Personalized Healthcare Systems

Technical Summary: Quantum Agentic Secure Communication for Adaptive and Personalized Healthcare Systems

Problem Statement
Next-generation personalized healthcare systems rely on continuous data streams from wearable biosensors, implantable devices, and medical IoT networks. These systems face a dual security challenge:

1. Adversarial Dynamics: Classical cryptographic assumptions are increasingly vulnerable to quantum-capable adversaries. Furthermore, existing Quantum Key Distribution (QKD) protocols often employ fixed defensive settings that cannot adapt when an attacker shifts strategies (e.g., from intercept-resend to photon-number-splitting attacks).
2. Resource Constraints: Healthcare edge devices (e.g., wearable sensors) have limited computational power, making complex multi-round negotiations and heavy post-processing (common in protocols like MDI-QKD or Twin-field QKD) impractical.
3. Static vs. Dynamic: Current frameworks treat QKD as a static security layer, failing to address the need for real-time, adaptive defense in volatile, noisy medical communication channels.

Methodology: The Strategic Quantum Game Mechanism (SQGM)
The paper proposes SQGM, a quantum-agentic framework that models healthcare entities (wearable sensors and hospital servers) as rational strategic agents playing a quantum game against an adaptive eavesdropper. The core methodology involves:

• Adaptive Strategy Selection: Unlike static protocols, SQGM allows communicating agents to continuously update their quantum encoding strategies based on observed channel conditions and attack patterns. This is achieved through the computation of a Nash Equilibrium (NE).
• System Architecture:
  • Game Strategy Module (GSM): Located on wearable devices and edge nodes, this module estimates channel noise ($\hat{p}$) and tracks the adversary's strategy distribution ($\pi_E$).
  • Equilibrium Decision Engine (EDE): Located on the hospital server, this engine coordinates NE computation over an authenticated classical channel.
  • Bayesian Defense: An adaptive Bayesian tracking mechanism monitors block-level Quantum Bit Error Rate (QBER) statistics. If the Kullback-Leibler (KL) divergence between the current and previous adversary strategy distributions exceeds a threshold, the system declares a strategy shift and triggers a re-computation of the NE in $O(\log n)$ time.
• Protocol Implementation:
  • The protocol utilizes a 5-qubit quantum circuit where qubits $q_0$–$q_1$ represent the sender, $q_2$–$q_3$ form a Bell-pair channel, and $q_4$ provides quantum authentication.
  • Payoff functions are defined for both the sender (maximizing Secret Key Rate (SKR) while minimizing QBER) and the eavesdropper (maximizing information gain while minimizing detection risk).
  • The NE solver (Algorithm 2) iteratively updates polar rotation angles ($\theta_A, \theta_E$) to find a stable operating point, converging within approximately 12 iterations.

Key Contributions

1. A Quantum-Agentic Framework: A unified system where medical entities act as adaptive strategic agents over a QKD channel, moving beyond static security layers.
2. The SQGM Protocol: A 5-qubit QKD framework featuring adaptive measurement-basis selection and quantum authentication, designed for lightweight hardware.
3. Game-Theoretic Strategy Module: A mechanism that dynamically updates defensive configurations based on observed channel conditions rather than fixed parameters.
4. Adaptive Bayesian Defense: A detection mechanism that identifies eavesdropper strategy changes and triggers equilibrium re-computation efficiently, suitable for edge-deployed medical hardware (e.g., ARM Cortex-M4).

Experimental Results
The framework was evaluated using the IBM Quantum 7-qubit noise model and published QKD field-trial data (SECOQC Vienna and Tokyo QKD Network). Key performance metrics at 100 km fiber distance ($p=0.05$, $n=10^6$ bits) include:

• QBER Reduction: SQGM achieved a QBER of 7.1%, representing a 39.6% reduction compared to the standard BB84 protocol (11.7%).
• Secret Key Rate (SKR): SQGM achieved an SKR of 91.7 kbps, a 24% improvement over BB84 (74.2 kbps).
• Attack Detection: The system demonstrated an Area Under the Curve (AUC) of 0.987 for attack detection, significantly outperforming BB84 (0.951).
• Latency: The execution time per 1,000 generated key bits was 2.31 ms, compatible with real-time medical data pipelines requiring sub-10 ms overhead.
• Security: The system achieved a composable security parameter ($\epsilon$) of $2.1 \times 10^{-11}$, meeting clinical-grade security requirements.
• Ablation Studies: The "Adaptive Defence" component contributed the largest single gain in SKR (+10.5 kbps), confirming the value of online strategy updates.

Significance and Claims
The paper claims that SQGM directly addresses the gap between static cryptographic defenses and dynamic adversarial attacks in healthcare. By embedding an adaptive, game-theoretic security layer into the QKD infrastructure, the framework offers:

• Enhanced Reliability: The reduction in QBER and increase in SKR improve the reliability and confidentiality of continuous medical data transmission (e.g., ECG, glucose monitoring, neural data).
• Real-Time Adaptability: The ability to detect strategy shifts and re-optimize defenses in real-time protects clinical AI agents from adversarially corrupted inputs.
• Hardware Feasibility: The lightweight nature of the NE solver (requiring only ~2 kB ROM and minimal floating-point operations) makes the framework viable for deployment on resource-constrained wearable medical devices.
• Long-Term Confidentiality: The framework supports long-term data confidentiality against "harvest now, decrypt later" attacks while simultaneously detecting active eavesdroppers.

The authors conclude that SQGM provides a unified, lightweight solution that reconciles the strict hardware constraints of medical IoT with the volatile dynamics of active adversary interception, offering a pathway for secure, adaptive personalized healthcare systems.