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PLS-Assisted Offloading for Edge Computing-Enabled Post-Quantum Security in Resource-Constrained Devices

Hamid Amiriara, Mahtab Mirmohseni, Rahim Tafazolli

TL;DR

This work tackles the latency and security challenges of deploying post-quantum cryptography on resource-constrained IoT devices by introducing a PLS-assisted, edge-computing offloading framework. By jointly optimizing transmit power, PQES compute allocation, and offloading decisions through an AO-SCA algorithm, the approach leverages wiretap coding for secure offloading and friendly jamming to degrade eavesdroppers, while accounting for CSI uncertainty. The proposed method achieves substantial latency reductions and remains robust under adversarial conditions, closely matching the No EVE upper bound in simulations. The results underscore the practical viability of secure PQC in IoT via cooperative edge computing and PLS techniques, with scalable performance as network size grows.

Abstract

With the advent of post-quantum cryptography (PQC) standards, it has become imperative for resource-constrained devices (RCDs) in the Internet of Things (IoT) to adopt these quantum-resistant protocols. However, the high computational overhead and the large key sizes associated with PQC make direct deployment on such devices impractical. To address this challenge, we propose an edge computing-enabled PQC framework that leverages a physical-layer security (PLS)-assisted offloading strategy, allowing devices to either offload intensive cryptographic tasks to a post-quantum edge server (PQES) or perform them locally. Furthermore, to ensure data confidentiality within the edge domain, our framework integrates two PLS techniques: offloading RCDs employ wiretap coding to secure data transmission, while non-offloading RCDs serve as friendly jammers by broadcasting artificial noise to disrupt potential eavesdroppers. Accordingly, we co-design the computation offloading and PLS strategy by jointly optimizing the device transmit power, PQES computation resource allocation, and offloading decisions to minimize overall latency under resource constraints. Numerical results demonstrate significant latency reductions compared to baseline schemes, confirming the scalability and efficiency of our approach for secure PQC operations in IoT networks.

PLS-Assisted Offloading for Edge Computing-Enabled Post-Quantum Security in Resource-Constrained Devices

TL;DR

This work tackles the latency and security challenges of deploying post-quantum cryptography on resource-constrained IoT devices by introducing a PLS-assisted, edge-computing offloading framework. By jointly optimizing transmit power, PQES compute allocation, and offloading decisions through an AO-SCA algorithm, the approach leverages wiretap coding for secure offloading and friendly jamming to degrade eavesdroppers, while accounting for CSI uncertainty. The proposed method achieves substantial latency reductions and remains robust under adversarial conditions, closely matching the No EVE upper bound in simulations. The results underscore the practical viability of secure PQC in IoT via cooperative edge computing and PLS techniques, with scalable performance as network size grows.

Abstract

With the advent of post-quantum cryptography (PQC) standards, it has become imperative for resource-constrained devices (RCDs) in the Internet of Things (IoT) to adopt these quantum-resistant protocols. However, the high computational overhead and the large key sizes associated with PQC make direct deployment on such devices impractical. To address this challenge, we propose an edge computing-enabled PQC framework that leverages a physical-layer security (PLS)-assisted offloading strategy, allowing devices to either offload intensive cryptographic tasks to a post-quantum edge server (PQES) or perform them locally. Furthermore, to ensure data confidentiality within the edge domain, our framework integrates two PLS techniques: offloading RCDs employ wiretap coding to secure data transmission, while non-offloading RCDs serve as friendly jammers by broadcasting artificial noise to disrupt potential eavesdroppers. Accordingly, we co-design the computation offloading and PLS strategy by jointly optimizing the device transmit power, PQES computation resource allocation, and offloading decisions to minimize overall latency under resource constraints. Numerical results demonstrate significant latency reductions compared to baseline schemes, confirming the scalability and efficiency of our approach for secure PQC operations in IoT networks.

Paper Structure

This paper contains 11 sections, 22 equations, 5 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. System Model
  4. Problem Formulation
  5. Proposed Solution
  6. Sub-problem 1: RCDs’ Transmit Power Optimization
  7. Sub-problem 2: Computing Capacity Allocation
  8. Sub-problem 3: Offloading Decision Optimization
  9. Overall Algorithm
  10. Simulation Results
  11. Conclusion

Figures (5)

  • Figure 1: PLS-assisted offloading in edge computing-enabled PQC systems.
  • Figure 2: System total latency versus transmit data size per RCD, $d_k$.
  • Figure 3: System total latency versus the PQES computing capacity, $f^\text{Tot}$.
  • Figure 4: System total latency versus versus the number of RCDs, $K$.
  • Figure : Proposed AO-SCA Algorithm for Problem ($\mathcal{P}$)