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Block4Forensic: An Integrated Lightweight Blockchain Framework for Forensics Applications of Connected Vehicles
Mumin Cebe, Enes Erdin, Kemal Akkaya, Hidayet Aksu, Selcuk Uluagac
TL;DR
Connected vehicles generate diverse data that could support post-accident forensic analysis, especially for determining responsibility in self-driving-vehicle incidents. The paper proposes Block4Forensic, a permissioned blockchain integrating VPKI and fragmented ledgers to manage this data with privacy and reduced overhead. Its framework supports traceable accident reconstruction using event, diagnostic, and maintenance data while storing hashes in the shared ledger and detailed records in fragmented ledgers.
Problem
Vehicle forensic evidence is distributed across connected-vehicle data sources, while post-accident analysis requires information for determining responsibility and resolving disputes.
Method
Block4Forensic integrates VPKI with a permissioned blockchain and uses fragmented ledgers for event, diagnostic, and maintenance data.
Results
The framework reconstructs accident scenarios, identifies faulty parties including a third vehicle or manufacturer, and supports hit-and-run analysis using timestamped, diagnostic, and safety data.
Takeaways & Limitations
B4F provides a trustless, traceable, and privacy-aware approach for comprehensive post-accident vehicular forensics across multiple participating parties.
Abstract
from arXiv · showhide
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.
I. INTRODUCTION
Connected vehicles combine sensing, control, and wireless communication to generate data useful for accident forensics, but existing evidence is incomplete and fragmented. Block4Forensic addresses these gaps with a privacy-aware blockchain framework involving relevant parties.
- Modern vehicles function as cyber-physical systems integrating sensors, electronic control units, onboard units, and networks such as CAN, LIN, FlexRay, and Bluetooth.
- Onboard diagnostics, event data recorders, onboard units, and dedicated short-range communications provide vehicle, incident, and connectivity data for forensic analysis.
- Existing accident evidence omits comprehensive vehicle history because stored data can be overwritten, while extraction requires trust in third parties.
- Current systems do not integrate evidence from vehicles, road conditions, manufacturers, and maintenance centers, and lack a dedicated solution for hit-and-run cases.
- Block4Forensic proposes a lightweight, privacy-aware blockchain that brings drivers, maintenance centers, manufacturers, and law enforcement together without requiring a trusted third party.
II. BACKGROUND
Vehicular forensics supplements physical accident evidence with data from vehicle digital subsystems, but collection and analysis lack universal standards. A useful framework must support convenient analysis while preserving user privacy and data transparency.
- Vehicular forensics combines physical scene evidence with vehicle-generated data from controllers and sensors to reconstruct accidents and assess responsibility.
- Investigators secure digital devices, examine incident-related traces, and analyze possible cyber attacks, component failures, and driver mistakes.
- There is no universal standard for collecting, examining, and analyzing data from vehicles, drivers, and involved units.
- An appropriate framework should enable convenient data collection and analysis while preserving user privacy and making stored content clear to its owner.
- EDRs capture crash-related vehicle states after triggering events and investigators commonly extract their data through onboard diagnostic ports.
3) Dedicated Short Range Communications (DSRC) and Basic Safety Messages (BSM):
DSRC supports connected-vehicle safety communication through authenticated messages, including BSMs carrying vehicle state and positional information. VPKI supplies certificate-based pseudonymous identities, while blockchain provides shared, cryptographically linked records.
- Dedicated Short Range Communications (DSRC) and Basic Safety Messages (BSM): BSMs convey safety information including vehicle position, speed, size, brake status, identifier, positional accuracy, and steering-wheel angle.
- Dedicated Short Range Communications (DSRC) and Basic Safety Messages (BSM): IEEE 1609.2 is used in connected-vehicle networking for message integrity and authentication.
- Dedicated Short Range Communications (DSRC) and Basic Safety Messages (BSM): VPKI manages vehicle certificates through a certification authority, with five-minute pseudonym validity supporting anonymity and resistance to targeted privacy and spoofing attacks.
- Blockchain: Blockchain links blocks cryptographically to preserve ordering, provide timestamping, and support a shared ledger without a central authority.
- Blockchain: Permissioned blockchains restrict participation to members, unlike public blockchains that allow unrestricted ledger reading and writing.
C. Current State of the Art in Vehicular Forensics
Vehicular forensics is expanding through commercial data collection and blockchain proposals, but existing work does not yet provide a comprehensive, applicable, and scalable framework. The paper identifies integrity, privacy, accountability, comprehensiveness, and low overhead as design requirements.
- Current State of the Art in Vehicular Forensics: Commercial tools such as Berla’s iVe collect data from EDR, OBD, infotainment, and telematics systems, with collected data stored in cloud storage.
- Current State of the Art in Vehicular Forensics: Prior blockchain work sketches vehicular-security use cases for insurance and software updates but lacks practical treatment of membership management and scalability.
- Current State of the Art in Vehicular Forensics: Vehicular forensics aims to resolve accident disputes and determine faulty parties by combining sensor data with information from manufacturers, drivers, insurers, and law enforcement.
- Current State of the Art in Vehicular Forensics: Required properties include forensic-data integrity, non-repudiation, reduced reliance on a single authority, comprehensive historical analysis, lightweight operation, and privacy.
- Current State of the Art in Vehicular Forensics: The system should preserve participant privacy while allowing participants to selectively reveal their data.
B. B4F Framework
B4F introduces a blockchain-based vehicular forensics framework connecting vehicles and stakeholders to support privacy-aware, integrity-verifiable post-accident investigation.
- The framework is presented as a novel blockchain forensic framework for connected vehicular stakeholders.
- B4F connects vehicles, maintenance providers, manufacturers, law enforcement, and insurance companies within one forensic framework.
- A forensic daemon in the OBU retrieves EDR, BSM, and sensor data, while manufacturers and maintenance providers submit hashes of diagnostic and maintenance records to the blockchain.Relevant EDR and BSM data are periodically shared with insurers through an encrypted channel; personal-cloud storage is optional.
- Stored data can be selectively disclosed after an accident so investigators can evaluate evidence with integrity proofs.
C. Potential accident scene
B4F data supports multiple accident-scene reconstructions, linking vehicle behavior, traffic signals, third-party vehicles, maintenance history, and autopilot diagnostics to possible fault assignments.
- Investigators use collected clues to reconstruct the accident scene and determine the faulty party.
- BSM messages can show that V1 turned left while the traffic light was red, identifying V1 as the faulty party.
- Timestamped B4F data can reveal V3 as a third vehicle that ran a red light and caused the collision, resolving a hit-and-run case.
- BSM data from smart traffic lights can identify faulty signalization when multiple vehicles have green lights and none violates traffic rules.
- Maintenance-record history can expose a post-maintenance braking problem and suggest the maintenance provider as the faulty party.
- Autopilot status combined with a failed sensor diagnosis can implicate autopilot software and suggest the car manufacturer as the faulty party.
IV. B4F COMPONENTS
B4F components center on an OBU-based forensic daemon that reads vehicle and external data, timestamps and signs selected records, and supports later disclosure during investigations.
- IV. B4F COMPONENTS: The components section covers forensic elements and data types alongside B4F blockchain membership and storage issues.
- A. Forensic Daemon: The forensic daemon runs as an OBU application and interacts with vehicle components through the vehicle network.
- A. Forensic Daemon: The OBU reads CAN-bus data covering systems such as steering, braking, throttle, tire pressure, seat belts, and windshield wipers.
- A. Forensic Daemon: The daemon can receive driver health data through Wi-Fi or Bluetooth and retrieve road and weather information from RSUs or a smartphone.
- A. Forensic Daemon: It collects data according to predefined or custom rules, adds timestamps, signs records with a pseudonym certificate, and discloses submissions during investigations.
B. Forensic data types and B4F process
B4F organizes event, diagnosis, and maintenance data across vehicles and collaborating parties, using a permissioned blockchain with shared hashes and fragmented content storage. Its participant roles and consensus process support traceable data integrity without relying on a trusted third party.
- Forensic data types: B4F distinguishes event, diagnosis, and maintenance data, with maintenance records signed by both the vehicle and maintenance provider.Event data is triggered by predefined EDR conditions; diagnosis data is produced periodically or after failures.
- B4F process: Forensic data content remains between relevant parties, while its hash, timestamp, and pseudonym vehicle ID are signed and submitted to corresponding organizations.The framework uses hashes in the shared ledger while detailed content is retained by parties such as insurers, manufacturers, or personal cloud storage.
- Blockchain structure: B4F uses permissioned blockchain technology with shared and fragmented ledgers to exchange information securely and efficiently among collaborating parties.The design separates shared blockchain records from data fragments held by participants.
- Blockchain structure: The network includes leaders, validators, monitor units, and vehicle clients, with validators including manufacturers, maintenance centers, and insurance companies.Leaders are randomly selected among validators, while vehicles provide signed transactions.
- Consensus and oversight: Validators use Byzantine agreement protocols such as PBFT to confirm blocks, while law-enforcement monitors retain shared-ledger replicas for post-accident disputes.The proposed framework is intended to increase trust among participants and eliminate the need for a trusted third party.
- Integrity and overhead: Hash-based records minimize replicated shared-ledger overhead and allow forensic-data integrity verification by comparing stored and corresponding hashes.The detailed forensic content is not necessarily replicated across all participants.
D. Integrated Membership Management and Privacy via Pseudonym Certificates
Permissioned membership control can conflict with privacy because participants use identifiable credentials. B4F addresses this tension with VPKI pseudonyms that rotate across time intervals while retaining certificate-based transaction validation.
- Membership and privacy: Permissioned blockchains restrict participation through membership services, but their issued identities do not by themselves protect privacy between interacting peers.This limits the anonymity available in public-blockchain identity models.
- Pseudonym certificates: B4F uses VPKI pseudonym identities as vehicle-client tokens to provide anonymity within the permissioned blockchain.The approach is based on the VPKI model suggested in IEEE 1609.2.
- Pseudonym certificates: Vehicle pseudonym identities change every five minutes, so successive transactions use different identities and help protect user privacy.The text notes that disclosure remains subject to applicable regulations and policies.
- Transaction validation: Validators check submitted hash certificates and timestamps, confirming transactions when timestamps fall within the certificate validity period.Consensus on valid transactions uses a computationally inexpensive voting-based Byzantine agreement scheme.
E. Lightweight Fragmented Ledger for Forensic Participants
B4F reduces blockchain storage demands by keeping forensic-data proofs in a shared ledger and detailed records in fragmented ledgers or personal storage. The authors identify unresolved availability, malicious-participant, implementation, and policy questions.
- Fragmented ledger rationale: Replicating immense forensic data across a shared ledger could cause substantial communication and storage overhead.Each blockchain participant stores a copy of the shared-ledger history.
- Fragmented ledger design: B4F stores hashes in the shared ledger while participants retain different forensic-data fragments according to their interests and responsibilities.For example, insurance companies may retain EDR content while the shared ledger stores proof of that content.
- Data disclosure boundary: Users may keep maintenance or manufacturer-data content in personal cloud storage, with authorities able to require disclosure under applicable regulations and policies.Blockchain provides integrity for the disclosed data.
- Future research: Additional research is needed to analyze B4F storage and communication overhead through implementation with an OBU software-development kit.The paper presents this as a future research issue rather than a completed evaluation.
- Future research: B4F leaves punishment, incentives, avoidance, and detection mechanisms for preventing or identifying malicious participants as future research topics.The stated target is malicious behavior by framework members.
- Availability limitation: Because B4F stores only hash values, forensic-data integrity and immutability are supported, but availability depends on individual and shared storage without blockchain enforcement.The authors state that ensuring availability of critical forensic data requires further research.
- Future research: The paper calls for forensic-by-design principles in future systems as vehicle-data availability and blockchain use expand.This is framed as a broader research consideration for forensic applications.
- Future research: Regulations governing entity participation and policies for using forensic blockchain data in criminal cases remain open research issues.The issue concerns enforcement and evidentiary use across participating organizations.
VI. CONCLUSION
The paper proposes a blockchain infrastructure for comprehensive accident-forensics services. Its design combines VPKI-based permissioned membership and pseudonyms with fragmented storage that keeps hashes shared and detailed records distributed.
- Conclusion: B4F constructs blockchain infrastructure for comprehensive forensic services in accident investigations.The conclusion presents this as the paper’s central proposal.
- Conclusion: VPKI addresses permissioned-blockchain membership management and storage overhead, while pseudonymous identities help preserve user privacy.The design stores hashed data in the shared ledger and detailed non-hashed data in fragmented ledgers.
- Conclusion: Fragmented ledgers retain detailed vehicle-related records outside the shared ledger, which stores their hashes.This separates proof of data from the underlying forensic content.