# Compp-DDPG: Computation Offloading with Privacy Preservation in a Vehicular Edge Network
## 2 Related Work  
A growing number of works have been devoted to computation offloading and resource allocation for delay minimization [17; 18], the trade-off between delay and fairness [19], handover management [20], and under-utilized resource exploration [21; 22; 23]. [PERSON] et al. [17] proposed a load-balancing computation offloading scheme for VECNs to reduce the task-processing delay by efficiently utilizing the edge resources. To cope with the dynamic and uncertain environment of VECNs, [PERSON] et al. [18] aimed to enable vehicles to decide on the optimal offloading strategy by learning the offloading delay performance of their neighbors. An adaptive-learning-based task offloading mechanism was designed based on the multi-armed bandit theory to minimize the average offloading delay. [PERSON] et al. [24] designed a two-stage VFC framework for joint resource management and task offloading, which consists of a contract theory-based vehicular computational resource management scheme and a matching-learning-based task offloading mechanism. [PERSON] et al. [19] proposed a collaborative method for optimal task offloading and resource allocation in VECN to achieve the trade-off between delay and fairness of collaboration. Considering the highly dynamic of VECN, the authors of [20] designed an intelligent task offloading scheme based on deep Q-learning to deal with the frequent handover with rapid changes in communication and computing. The studies in [21; 22; 23] aimed to exploit the under-utilized resources of the nearby vehicles. In [21], the authors focused on exploiting multi-hop vehicle computational resources for a task-offloading approach in VECNs based on the mobility analysis of vehicles. [PERSON] et al. [22] focused on resource allocation and task assignment jointly by designing a cooperative vehicular fog computing architecture from an overlapping perspective, to fully utilize the under-explored resources of nearby vehicles. In [23], the authors proposed a joint offloading and resource allocation scheme for the parked-and-moving-vehicles-assisted VECN. This work aims to minimize the total offloading delay by employing a two-stage heuristic algorithm to determine the optimal strategies for offloading, channel allocation, and resource allocation. However, the above-mentioned works mainly focus on computation offloading without considering the threat of privacy disclosure during computation offloading. Some studies are aware that user privacy leaks during the computation offloading [25; 26; 27; 28]. [PERSON] et al. [25] aimed at minimizing the offloading action and transmitting power with the objective of minimizing the system's cost under the privacy requirement of task offloading. [PERSON] et al. [26] proposed a privacy-preserving VEC framework to minimize the delay of task execution by jointly optimizing the task offloading and resource-allocation algorithm. [PERSON] et al. [27] adopted the non-dominated sorting genetic algorithm II (NSGA-II) for multi-objective optimization of execution time, energy consumption, and the privacy of the computing tasks in the VECN. A privacy-oriented task offloading method was designed in [28] based on deep reinforcement learning to resist attacks from privacy attackers with prior knowledge. However, these mechanisms formulate the privacy level as an indicator or the constraint of the optimization problem but lack the design of a privacy protection strategy for vehicles to defend the offloading actively against the privacy threats. From the perspective of theoretical mechanisms that are applied for problem solutions, most of the above studies adopted the methods of game theory [17; 23; 25], contract theory and a matching mechanism [19], heuristic approaches [23; 24; 27], or an optimization approach [22; 26].