Abstract

Abstract—Vehicular edge computing (VEC) faces critical
challenges regarding limited onboard computational capacity
and the potential for location privacy leakage. To address these
issues, this paper proposes a novel, privacy-aware task
offloading framework driven by deep reinforcement learning
(DRL). First, we design a dynamic caching mechanism at
roadside units (RSUs) to store previously computed task
outputs, reducing redundant computation and communication
overhead. Second, we introduce privacy entropy to
quantitatively measure and enforce location privacy protection,
mitigating the risk of RSU-induced data breaches. We then
formulate a joint optimization problem that balances latency,
energy consumption, and privacy preservation. To efficiently
solve this, a deep deterministic policy gradient algorithm
enhanced with prioritized experience replay (PER-DDPG) is
developed to learn the optimal offloading strategy. Simulation
results demonstrate that our proposed approach outperforms
existing benchmark schemes, delivering superior convergence
speed, lower latency, higher energy efficiency, higher privacy
entropy and an improved task success rate.