Block4Forensic: An Integrated Lightweight Blockchain Framework for Forensics Applications of Connected Vehicles

TLDR

Vehicles are evolving into cyber‑physical systems that collect extensive sensor data, enabling smart, connected cars that generate valuable information for drivers, manufacturers, insurers, and service providers, and making forensic analysis of traffic accidents increasingly critical. The authors propose a permissioned blockchain framework to manage the diverse vehicle‑related data collected by these systems. The framework integrates vehicular public key infrastructure for membership and privacy, and employs a fragmented ledger to store detailed vehicle information such as maintenance history and diagnostic reports. The resulting forensic system offers trustless, traceable, and privacy‑aware post‑accident analysis with minimal storage and processing overhead.

Abstract

Today's vehicles are becoming cyber-physical systems that not only communicate with other vehicles but also gather various information from hundreds of sensors within them. These developments help create smart and connected (e.g., self-driving) vehicles that will introduce significant information to drivers, manufacturers, insurance companies, and maintenance service providers for various applications. One such application that is becoming crucial with the introduction of self-driving cars is forensic analysis of traffic accidents. The utilization of vehicle-related data can be instrumental in post-accident scenarios to discover the faulty party, particularly for self-driving vehicles. With the opportunity of being able to access various information in cars, we propose a permissioned blockchain framework among the various elements involved to manage the collected vehicle-related data. Specifically, we first integrate vehicular public key infrastructure (VPKI) to the proposed blockchain to provide membership establishment and privacy. Next, we design a fragmented ledger that will store detailed data related to vehicles such as maintenance information/ history, car diagnosis reports, and so on. The proposed forensic framework enables trustless, traceable, and privacy-aware post-accident analysis with minimal storage and processing overhead.