A blockchain-based scalable authentication framework for secure data sharing in internet of vehicles

The Internet of Vehicles (IoV) enables vehicles to exchange real-time information using wireless communication and onboard sensors; however, ensuring secure and efficient authentication for large-scale data sharing remains a significant challenge. Current authentication approaches often experience high processing costs, increased memory consumption, and insufficient detection accuracy, making them unsuitable for deployment in large-scale IoV environments. To address these issues, a blockchain-based efficient authentication approach is devised for data sharing amongst the vehicles. The entities included in the proposed system are Road Side Units (RSU), issuers, Vehicles, Traffic Management Authority (TMA), Law enforcement department (LED), and tracers. The steps followed by the proposed model include initialization, key generation, registration, message generation, encryption with data sharing and authentication. TMA initializes the auxiliary and parent blockchain in each region. In key generation, the public and private keys are generated for authentication. Next, the vehicle is registered with the TMA in the registration phase. Then, the message is recorded with blockchain. Once the message is recoded, the encryption and data sharing phase is executed for secure sharing. After that, the authentication is carried out to select a genuine vehicle for data sharing. Performance evaluation is conducted using computation time, detection rate, memory usage, communication overhead, and blockchain transaction latency. For 50 devices, the proposed approach achieves a computation time of 0.065 s. The detection rate reaches 91.455%, showing an improvement of 4.37–10.50%, while memory usage is reduced to 3.792 MB, achieving a reduction of 4.5–15.7%. It also achieves the minimum communication overhead of 2.383 KB, obtaining a reduction of 5.47–45.30%. Similarly, the lowest blockchain transaction latency of 19.655 ms is achieved, corresponding to a reduction of 16.60–43.44%. These results demonstrate that the proposed framework consistently outperforms existing methods in terms of efficiency and scalability for practical IoV applications.