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

TL;DR

This paper introduces QuHE, a novel algorithm that optimizes the trade-offs among quantum key distribution utility, homomorphic encryption security, and system costs in secure edge computing networks.

Contribution

It presents a new integrated framework combining QKD, transciphering, and HE, along with an efficient algorithm for resource allocation in secure MEC systems.

Findings

QuHE effectively balances security, utility, and costs.

Theoretical analysis confirms convergence and optimality of QuHE.

Simulations show improved performance across multiple metrics.

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.