Distributed-Ledger Based Event Attestation For Intelligent Transportation Systems

The presence of intelligent and autonomous vehicles on public roads is no longer science fiction. Guaranteeing the security of these networks and the data exchanged is key to ensuring the safety and privacy of passengers on public roads. The use of conventional infrastructure and technologies for intelligent transportation systems (ITS) results in centralized environments with potential privacy concerns and are vulnerable to spoofing and distributed denial-of-service (DDoS) attacks. We contend that some of these vulnerabilities can be mitigated via distributed-ledger technologies (DLT) such as blockchain (BC) and directed acyclic graphs (DAG). By using an append-only ledger system, it is possible to establish consensus among peers with regards to which transactions to keep as well as their validity in a peer-to-peer (P2P) fashion. A consortium based DLT framework would provide some level of trust in a distributed ledger network (DLN) by only allowing consensus among preselected nodes. The use of such technology as part of an ITS managed by a consortium of transportation organizations (e.g., government, insurance companies, vehicle manufacturers) would provide decentralized computing, authentication, validation, consensus, and fault tolerance at much lower costs than alternative solutions. As an example use case, it could provide attestation of transportation-related events such as traffic jams and accidents as well as snapshots of vehicle information such as maintenance records or odometer readings. This paper explores the use of a consortium DLT-based architecture to augment vehicle-to-infrastructure (V2I)/ITS capabilities and serve as the source of event attestation. It describes the effectiveness of this technology in securing against different types of attacks and expected implementation challenges. It also discusses how this approach could provide a form of privacy control to vehicle owners. Finally, it proposes additional DLT research that could lead to improvements in scalability, consensus and validation mechanisms, and incentives for organizations involved.