A homomorphic encryption-based privacy preservation for adaptive quantum cross-task optimization

Abstract Vehicular networks increasingly necessitate a robust paradigm that harmonizes high-performance multi-task optimization with stringent data transmission privacy. Nevertheless, current research often struggles to achieve an ideal equilibrium between rigorous security, efficient coordination logic, and the constrained computational capacities of on-board units. To surmount these hurdles, this paper proposes a homomorphic encryption-based privacy preservation for adaptive quantum cross-task optimization (QHE-PSO). The proposed scheme synergistically integrates localized identity authentication, homomorphic fitness appraisal, and a cross-domain strategy migration mechanism. By managing encrypted particle swarms in distinct local regions where particles represent specific strategy configurations, QHE-PSO implements a secure migration protocol to facilitate seamless multi-task coordination. Extensive experimental evaluations confirm that QHE-PSO delivers a superior balance of cryptographic resilience and optimization efficacy compared to state-of-the-art benchmarks.