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Article

Towards Secure, Decentralised, and Privacy Friendly Forensic Analysis of Vehicular Data

1
Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131 Lamia, Greece
2
Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU), NO-2802 Gjovik, Norway
*
Author to whom correspondence should be addressed.
Academic Editors: Thomas K. Dasaklis, Fran Casino and Rachaniotis Nikolaos
Sensors 2021, 21(21), 6981; https://doi.org/10.3390/s21216981
Received: 28 August 2021 / Revised: 28 September 2021 / Accepted: 5 October 2021 / Published: 21 October 2021
The automotive industry has been transformed through technological progress during the past decade. Vehicles are equipped with multiple computing devices that offer safety, driving assistance, or multimedia services. Despite these advancements, when an incident occurs, such as a car crash, the involved parties often do not take advantage of the technological capabilities of modern vehicles and attempt to assign liability for the incident to a specific vehicle based upon witness statements. In this paper, we propose a secure, decentralized, blockchain-based platform that can be employed to store encrypted position and velocity values for vehicles in a smart city environment. Such data can be decrypted when the need arises, either through the vehicle driver’s consent or through the consensus of different authorities. The proposed platform also offers an automated way to resolve disputes between involved parties. A simulation has been conducted upon a mobility traffic dataset for a typical day in the city of Cologne to assess the applicability of the proposed methodology to real-world scenarios and the infrastructure requirements that such an application would have. View Full-Text
Keywords: forensics; vehicular; blockchain; integrity; automation; privacy; traffic; simulation forensics; vehicular; blockchain; integrity; automation; privacy; traffic; simulation
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MDPI and ACS Style

Negka, L.; Spathoulas, G. Towards Secure, Decentralised, and Privacy Friendly Forensic Analysis of Vehicular Data. Sensors 2021, 21, 6981. https://doi.org/10.3390/s21216981

AMA Style

Negka L, Spathoulas G. Towards Secure, Decentralised, and Privacy Friendly Forensic Analysis of Vehicular Data. Sensors. 2021; 21(21):6981. https://doi.org/10.3390/s21216981

Chicago/Turabian Style

Negka, Lydia, and Georgios Spathoulas. 2021. "Towards Secure, Decentralised, and Privacy Friendly Forensic Analysis of Vehicular Data" Sensors 21, no. 21: 6981. https://doi.org/10.3390/s21216981

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