Public-key infrastructure (PKI) provides the essential foundation for public-key cryptography, and security is often bootstrapped from a PKI. Standardization bodies, organizations, researchers, and experts reached a consensus that deploying Vehicular PKI (VPKI) can ensure the security and privacy requirements of Cooperative Intelligent Transportation Systems (C-ITS). Given the importance of VPKI, researchers and experts have worked to ensure VPKI meets the high security and privacy requirements of C-ITS. This survey focuses on VPKI schemes designed to securely and privately manage and revoke public-key certificates in three parts. The first part presents evaluation metrics that help to compare the existing VPKI proposals systematically. The second part classifies VPKI schemes and provides an overview of each. The third part focuses on observations made using the evaluation metrics through a systematic comparison of VPKI schemes. Finally, suggestions for future VPKI research are presented.

Driver identification faces various challenges in real-time applications. These challenges include high dimensional input data, moderate accuracy, scalability issues and need for custom-built in-vehicle sensors. The conventional biometric solutions are either less accurate, unscalable, or costly for practical application. In those solutions, high accuracy comes at the cost of preservation of privacy. Similarly, scalability and cost-effectiveness are inversely proportional to one another. This paper proposes a driver identification scheme to pare these challenges. The scheme uses data from the global positioning system (GPS) to learn an individual's driving pattern, which is commonly deployed in in-car navigation systems and can also be found in general purpose hand-held devices in the prevailing market. The proposed scheme is innovative in terms of providing a single solution for the existing challenges. It is more practical and scalable with large numbers of the drivers also being cost-effective and accurate, which makes it applicable in real-time applications with least overhead costs for different resources. The consequent analysis and empirical results show that the scheme could identify drivers with significant accuracy given only GPS data. To assess the scheme in depth, we perform experiments both on the collected data and the real-world open datasets. The average accuracy approximates above 96% for up to 25 drivers.

Vehicular social networking is expected to become a reality soon, where secure and privacy-preserving identity management is a prerequisite. Various organizations and studies have recommended using Vehicular Public-key Infrastructure (VPKI) to securely and privately manage vehicles' identities. However, the existing VPKI schemes still do not deal with identity management in vehicular social networking. In this work, we propose a Privacy-Preserving Identity Management (PPTIM) scheme for vehicular social networking to securely and privately manage vehicles' social identities. PPTIM builds on the transparency log (distributed ledger) proposals for web PKI, but it addresses challenges specific to vehicular social networking. All pseudo-social identities, pseudonym certificates issued against pseudo-social identities, revocation process, and revocation status validation are conducted on a transparency log. With an efficient data structure named the Accumulation Tree (AT), we extend the conventional transparency log to offer trustworthy pseudonym certificate validation without relying on the Certificate Revocation Lists (CRLs). In addition, PPTIM strengthens the security of vehicular social networking by protecting vehicles to a greater extent from being duped by malicious pseudonym certificates through logging and constant monitoring. Finally, performance and security analysis show the cost-effectiveness and security of PPTIM.

Driver identification and impostor detection suffer different challenges, including costly and invasive data collection. Existing methods incur additional costs due to their data dependency on complex and expensive sensory systems. This article proposes an event-driven framework for driver identification and impostor detection. That utilizes the Global Positioning System as the only data source. The proposed framework uses the Support Vector Machine as the supervised classification method. A modified RBF kernel is proposed to pursue a highly accurate framework for a large number of drivers. For the impostor detection task, this research proposes the use of a generalized negative class and devise an algorithm for building one. Two sets of experiments are performed to test the framework using a publicly accessible dataset and an indigenously collected dataset. The empirical study shows an average 94% recall for ten drivers and 90% for 24 drivers. Impostor driver is detected with more than 90% accuracy for more than three drivers.