# Compp-DDPG: Computation Offloading with Privacy Preservation in a Vehicular Edge Network
## 1 Introduction  
The development of beyond 5G (B5G) networks [1] and vehicular networks (VNs) and the ever-increasing vehicles on the road hasten the flourishing of vehicular applications, such as online gaming [2], autonomous driving [3], and augmented reality [4], which is generally computation intensive and delay-sensitive [5, 6, 7]. Although cloud computing provides powerful computational capabilities, remote resources could lead to high latency due to unpredictable transmission latency. By deploying the cloud capabilities close to the end-users, mobile edge computing (MEC) is emerging as a promising solution to reduce the long latency between the users and the cloud [8]. Vehicular edge computing (VEC) is further emerging as a prospective way to support the stringent requirements of vehicular applications by installing the MEC servers on roadside units (RSUs) to support the stringent requirements of vehicular applications [9]. Thanks to the closely deployed resources, the communication and computing delay could be significantly reduced by offloading the tasks to the VEC servers [10]. However, the VEC network is characterized by the salient features of both MEC and VNs, which brings several challenges for efficient task offloading and resource allocation in the VEC network (VECN) [11]. First, compared to conventional cloud servers, VEC servers have limited resources, which may be insufficient to meet the strict requirements of vehicular applications, especially for the explosive arrival of applications in dense scenarios. Second, the high mobility and dynamics of VNs lead to spatial-temporally dynamic and uneven distribution of tasks, which further brings difficulties for designing a dynamic and elastic task offloading and resource allocation mechanism for a VECN [12]. Third, some vehicular applications are safety-related and life-critical and should be processed preferentially, and some are non-safety related (such as the entertainment applications) and could be deferred, which makes the traditional offloading strategy infeasible for the heterogenous tasks with different priorities [13]. Fourth, besides the above-mentioned concerns on the quality of service (QoS), the privacy issue is critical but generally isolated from task offloading and resource allocation in VECNs [14; 15]. The highly dynamic and open nature of VN channels, together with the frequent task offloading behaviors of vehicles, make it possible for an attacker to infer the location or personal information of the vehicle users, leading to the threat of privacy disclosure [16]. The leakage of privacy information poses significant risks to the social privacy of drivers. Therefore, designing an efficient and dynamic task-offloading and computational-resource-allocation approach that is able to ensure the stringent and heterogenous requirements of vehicles, satisfy the resource constraints of VEC servers, and protect the privacy of vehicles in real-time is a fundamental problem. In this work, we propose a dynamic task-offloading and computational-resource-allocation approach based on decentralized multi-agent deep reinforcement learning (DRL), where privacy preservation for computation offloading, the priority of different tasks, the high mobility of ICVs, and the random arrival of the vehicular tasks are jointly considered. The main contributions of this work follow:  
1. We employ a privacy-preserving computation-offloading framework that integrates the security component of the certificate authority (CA) into the edge-servers-assisted vehicular network, which involves multiple RSUs and intelligent connected vehicles (ICVs). In this proposed system model, due to the high mobility of multi-ICVs with random arrival tasks, the offloading strategies, computational resources assigned to the ICVs, and the pseudonym-changing decisions vary with time slots. 2.