## 2 Related Works
### Previous Research on VCC  
This subsection reviews previous works related to VCC services and security. [PERSON] _et al._[12] integrated the Vehicular Cyber-Physical System (VCPS) with Mobile Cloud Computing (MCC) and proposed the VCPS and MCC Integration Architecture (VCMIA) to support ITS and cloud services for fluent traffic. VCMIA provided a mobile service to the user to access a mobile traffic cloud. In addition, using GIS with Traffic-Aware Capability and Cloud-Supported Dynamic Vehicle Routing, it provided real time traffic information to the user and shared road traffic information with other users. In addition, it utilized MCC and location information to provide optimal Vehicle Routing to the user. [PERSON] _et al._[17] proposed the VCPS for fluent and integrated communication between the vehicle network and customer center, and proposed situation recognition VCPS architecture using the cloud. Context-aware VCPS modified the parking service according to the situation and assigned computing resources to users in the VCPS data center. In addition, it provided functions such as Vehicle to Clouds(VTC) and Vehicle as Clouds (VAC) that are formed and provided by the Vehicle with Clouds (VWC) infrastructure and its users. VCC uses various computing capabilities of cloud computing paradigm. Vehicles with more stationary than moving time could use many public services on the road and parking lots. In addition, through cloud computing, various services such as traffic management, accident management, and entertainment are provided. [PERSON] _et al._[PERSON] _et al._ (2015) proposed the user-oriented Cloud Transportation System (C_TS) using a driving path guide. Crowdsourcing methods and cloud computing architecture are utilized to establish a traffic model that predicts by collecting, filtering, and modeling user data. User data (location and speed) are collected to determine user patterns based on a crowdsourcing method. The data are then calculated again, and, to provide real-time guidance, services are provided using an external data source. [PERSON] _et al._[PERSON] _et al._ (2015) distributed content efficiently to consumers through Named Data Networking (NDN), using names instead of IP addresses. They proposed a model consisting of the consumer requesting content and providers providing the information to the consumers in response. The cloud services that are generated and maintained by the cloud information providers were classified into existing cloud and vehicle cloud services. The cloud resources in the vehicle cannot only share data through storage, sensing, and computing, but can also detect and control event occurrences within the physical limits of the sensor capabilities. [PERSON] _et al._[PERSON] _et al._ (2015) used the network or storage of stationary vehicles in VCC to use idle resources. This system uses a Virtual Cord Protocol (VCP) to enroll a new vehicle or exclude an existing vehicle from the network. Driving and Parking modes are defined, and when a vehicle is in Driving mode, it accesses the VCP domain using an inter-domain routing for data exchange. The vehicle is enrolled using the existing code in parking mode. Inter-domain routing selects a vehicle to play the role of gateway for communication among multiple VCP cords, or decides which protocol to use when data is saved to a local VCP cord. [PERSON] and [PERSON] [PERSON] and [PERSON] (2016) addressed the problems of privacy, anonymous withdrawal, and route tracing via VANET using Clouds (VuC). The proposed technique protects privacy through multiple anonymities guaranteeing conditional anonymity. Beacon messages are saved in the cloud to use for route tracing and anonymous withdrawal. Institutions with authority can work together to trace a route selected by a user for a certain period of time. In addition, the institution can identify the user. [PERSON] _et al._[PERSON] _et al._ (2017) proposed a new VCC-based secure and privacy-preserving navigation protocol.