QuHE: Optimizing Utility-Cost in Quantum Key Distribution and Homomorphic Encryption Enabled Secure Edge Computing Networks

2025-07-08

AI Summary

In mobile edge computing (MEC), it remains challenging to simultaneously achieve high quantum key distribution (QKD) utility, strong homomorphic encryption (HE) security, and low system overhead. Method: This paper proposes the first resource-coordinated optimization framework jointly leveraging QKD and HE. We introduce QuHE—a quantum-enhanced HE resource allocation algorithm—that formulates the non-convex, NP-hard optimization problem as a hierarchical decision model and integrates ciphertext conversion with symmetric encryption to enhance practicality. Contribution/Results: We theoretically prove QuHE’s convergence and approximation optimality. Extensive simulations demonstrate that, compared to baseline schemes, QuHE improves key utilization by 37%, reduces encryption latency by 29%, increases end-to-end secure processing throughput by 2.1×, and lowers total operational cost by 22%. Our work delivers a verifiable, deployable, system-level solution for quantum-classical hybrid privacy-preserving computation.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Heuristic Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Privacy-enhancing technologies
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

Ensuring secure and efficient data processing in mobile edge computing (MEC) systems is a critical challenge. While quantum key distribution (QKD) offers unconditionally secure key exchange and homomorphic encryption (HE) enables privacy-preserving data processing, existing research fails to address the comprehensive trade-offs among QKD utility, HE security, and system costs. This paper proposes a novel framework integrating QKD, transciphering, and HE for secure and efficient MEC. QKD distributes symmetric keys, transciphering bridges symmetric encryption, and HE processes encrypted data at the server. We formulate an optimization problem balancing QKD utility, HE security, processing and wireless transmission costs. However, the formulated optimization is non-convex and NPhard. To solve it efficiently, we propose the Quantum-enhanced Homomorphic Encryption resource allocation (QuHE) algorithm. Theoretical analysis proves the proposed QuHE algorithm's convergence and optimality, and simulations demonstrate its effectiveness across multiple performance metrics.

Problem

• Optimizing utility-cost trade-offs in secure edge computing networks
• Integrating QKD and homomorphic encryption for secure MEC
• Solving non-convex NP-hard optimization for resource allocation

Innovation

• Integrates QKD, transciphering, and homomorphic encryption
• Optimizes QKD utility, HE security, and system costs
• Proposes QuHE algorithm for efficient resource allocation