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

TL;DR

This paper introduces Block4Forensic, a lightweight blockchain framework designed for secure, privacy-aware forensic analysis of connected vehicle data, integrating VPKI and a fragmented ledger to support post-accident investigations.

Contribution

It presents a novel integrated blockchain framework with VPKI and fragmented ledger for efficient, privacy-preserving vehicle data management in forensic applications.

Findings

Enables trustless and traceable post-accident analysis.

Reduces storage and processing overhead.

Supports privacy-aware data management.

Abstract

Today's vehicles are becoming cyber-physical systems that do 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 the forensic analysis for traffic accidents. The utilization of vehicle-related data can be instrumental in post-accident scenarios to find out the faulty party, particularly for self-driving vehicles. With the opportunity of being able to access various information on the 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 Management (VPKI) to the proposed blockchain to provide membership establishment and privacy. Next, we design a fragmented ledger that will store detailed data related to vehicle such as maintenance information/history, car diagnosis reports, etc. The proposed forensic framework enables trustless, traceable and privacy-aware post-accident analysis with minimal storage and processing overhead.