An innovative privacy preservation and security framework with fog nodes in enabled vanet system using hybrid encryption techniques

Effective communication between smart transportation and smart vehicles is carried out using Vehicular Ad-Hoc Networks (VANETs). Here, the VANET systems that exist nowadays have issues regarding user privacy and authentication. In internal vehicles, the fake message broadcasting should be stopped to protect the vulnerability of these vehicles from privacy issues. Additionally, the traditional manner of storing transmitted data lacks a decentralized and distributed security system, making it easily vulnerable for third parties to provoke malicious activities within the VANET system. VANET is an autonomous and open-access network, so, privacy and security are the main issues. Hence, it is essential to rectify the complications that are present in the traditional security and privacy preservation models in the VANET system. Thus, an innovative privacy preservation and security scheme with fog enabled VANET system is implemented by considering the complications in the existing models. The major that take place in the recommended framework are (a) Node Authentication, (b) Privacy Preservation, and (c) Message Verification. Initially, the node authentication is performed in the recommended framework using an Adaptive Deep Bayesian network (ADBN) in order to ensure an enhanced permissibility rate in the vehicular node. Then, messages are authenticated to protect the virtue of the messages. The parameters in the ADBN are tuned using the aid of Integrated Fire Hawk with Tunicate Swarm Algorithm (IFHTSA). Next, the privacy preservation procedure in the VANET model is carried out using Hybrid Attribute-Based Advanced Encryption Standard (HABAES) encryption techniques. The keys obtained on the encryption of the messages are signed digitally. Moreover, the suggested model utilized the fog node for the analysis instead of Road-Side Units (RSUs), because of its effectiveness in minimizing the latency rate with an increased throughput rate. In the message verification node, once the Fog Edge Node (FEN) receives the signed message from the vehicles, then it checks the validity of the vehicle node by comparing it with the signed messages. Finally, the experimentation is done based on various standard performance metrics. However, the developed model achieves 94% and 93% in terms of accuracy and precision. Hence, the suggested technique offers minimal computation and communication overhead in different experimental observations over the classical technique.