quantum-iomt-security

name	quantum-iomt-security
description	Post-quantum cryptography and edge-native security patterns for Internet of Medical Things (IoMT) systems, including federated learning, PQC migration, and Kubernetes-based orchestration.
version	1.0.0
author	Hermes Research
date	2026-07-08T00:00:00.000Z
category	medical

Quantum IoMT Security Patterns

Methodologies from recent research (June 2026) on securing Internet of Medical Things systems in the post-quantum era using edge-native architectures and post-quantum cryptography.

Core Patterns

1. Post-Quantum Cryptography for IoMT (PQC-IoMT Framework)

Description: Integration of NIST-standard Post-Quantum Cryptography (PQC) into resource-constrained IoMT devices for long-term security against quantum computing threats. Key Insights:

• Threat Model: "Harvest Now, Decrypt Later" (HNDL) attacks where adversaries collect encrypted medical data now and decrypt it once quantum computers are powerful enough.
• Algorithm Selection: NIST-standard algorithms (ML-KEM for key exchange, ML-DSA for signatures) must be adapted for IoMT devices with limited memory, processing, and battery.
• Hybrid Approach: Deploy hybrid classical+PQC schemes during transition, with classical algorithms as fallback during PQC migration.

2. Edge-Native Federated Learning Security

Description: Kubernetes-based orchestration of federated learning pipelines for IoMT with PQC-secured model update exchanges. Key Insights:

• Architecture: Raspberry Pi testbed validates scalable Kubernetes-based framework integrating PQC into FL-enabled IoMT environments.
• Optimization: Distributed cryptographic processing significantly reduces latency compared to sequential designs while maintaining feasible resource overhead.
• Threat: FL model updates may unintentionally expose private medical information; PQC protects these exchanges.

3. Distributed PQC Processing

Description: Breaking PQC operations across multiple edge nodes to reduce per-device computational burden while maintaining end-to-end security. Key Insights:

• Problem: Full PQC operations (key generation, encapsulation, decapsulation) exceed IoMT device capabilities.
• Solution: Distribute PQC workload across edge gateway nodes, with lightweight devices performing only the minimal PQC primitives.
• Result: Distributed design reduces latency while maintaining security guarantees for FL model updates.

Implementation Workflow

1. Assessment:

   • Inventory all IoMT devices and their cryptographic dependencies.
   • Identify devices handling long-lived sensitive data (most vulnerable to HNDL).
   • Assess computational headroom for PQC algorithm execution.

2. Migration Path:

   • Phase 1: Deploy PQC for long-lived data channels (TLS 1.3 with ML-KEM).
   • Phase 2: Add PQC signatures (ML-DSA) for device authentication.
   • Phase 3: Deploy hybrid classical+PQC during transition period.
   • Phase 4: Remove classical algorithms once PQC-only mode is stable.

3. Edge Architecture:

   • Deploy Kubernetes cluster for orchestration of FL pipelines.
   • Use distributed PQC processing: edge gateways handle heavy PQC operations.
   • Lightweight IoMT devices use minimal PQC primitives only.

4. Monitoring:

   • Track PQC operation latency and resource usage per device class.
   • Monitor for FL model poisoning attacks even with PQC transport security.
   • Implement energy-aware orchestration for battery-constrained devices.

References

• 2606.14515 - Securing the Future of IoMT in the Post-Quantum Era: An Edge-Native Federated Learning Approach (Alshoghri et al.)