A Differential Privacy Based Task Offloading Algorithm for Vehicular Edge Computing

With the advent of vehicular ad hoc networks (VANETs), vehicular edge computing (VEC) facilitates the execution of vehicular tasks through the Internet. In the VEC architecture, vehicles request task offloading, and a central decision center allocates resources. Effective task offloading algorithms provide optimal and equitable decisions based on objectives, such as task latency and system overhead; however, current task offloading algorithms for VEC face challenges in adapting to complex and dynamic road environments. This paper proposes a task-offloading algorithm based on deep reinforcement learning (RL) to address the challenges of task offloading in VEC. During the task offloading process, the privacy of vehicular task data may be compromised. This study introduces a novel task-offloading algorithm for VEC that employs differential privacy (DP) principles to safeguard the confidentiality of vehicular tasks during the offloading process. The proposed algorithm introduces noise in accordance with the privacy budget during the training process. The study provides a theoretical analysis of privacy, and experimental results based on Attari demonstrate that the proposed DP-based RL algorithm exhibits superior convergence compared to existing algorithms. Veins-based simulation experiments on VEC demonstrate that the proposed DP-based task-offloading algorithm can achieve practical offloading while preserving privacy.