The growing developments in vehicular networks and vehicular Internet services have yielded a variety of computation-intensive applications, resulting in great pressure on vehicles equipped with limited computation resources. The cloud/ edge-based service, which enables in-motion vehicles to actively offload computation tasks to cloud/ edge servers, has provided a promising approach to address the intensive computation burden. However, due to the possibility of disclosing private data, offloading computation tasks suffers from potential eavesdropping attacks. In this article, we focus on the eavesdropping attack when vehicular users (VUs) deliver computation tasks to cloud/edge servers over radio frequency channels. We take the tool of physical layer security and investigate resource management for secrecy provisioning when the VUs offload computation tasks. We then discuss three promising technologies, including non-orthogonal multiple access, multi-access assisted computation offloading, and mobility- and delay-aware offloading, which facilitate the enhancement of secrecy against the eavesdropping attack. Finally, as a detailed example of the multi-access assisted computation offloading, we present a case study on the optimal dual-connectivity- assisted computation task offloading with secrecy provisioning and show the performance of the proposed computation offloading.

中文翻译：

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车载计算卸载网络的保密驱动资源管理

车载网络和车载Internet服务的不断发展已产生了各种计算密集型应用程序，这给配备有限计算资源的车辆带来了巨大压力。基于云/边缘的服务使运动中的车辆能够主动将计算任务卸载到云/边缘服务器，这为解决密集的计算负担提供了一种有前途的方法。但是，由于可能会泄露私有数据，因此卸载计算任务会遭受潜在的窃听攻击。在本文中，我们将重点关注车载用户（VU）通过射频通道将计算任务传递给云/边缘服务器时的窃听攻击。我们使用物理层安全性工具，并在VU卸载计算任务时调查资源管理以进行保密配置。然后，我们讨论了三种有前途的技术，包括非正交多路访问，多路访问辅助计算分载，以及具有移动性和时延感知的分载，它们有助于增强针对窃听攻击的保密性。最后，作为多访问辅助计算分流的详细示例，我们提供了带有保密配置的最优双连接辅助计算任务分流的案例研究，并展示了所提出的计算分流的性能。这有助于增强针对窃听攻击的机密性。最后，作为多访问辅助计算分流的详细示例，我们提供了带有保密配置的最优双连接辅助计算任务分流的案例研究，并展示了所提出的计算分流的性能。这有助于增强针对窃听攻击的机密性。最后，作为多访问辅助计算分流的详细示例，我们提供了带有保密配置的最优双连接辅助计算任务分流的案例研究，并展示了所提出的计算分流的性能。