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

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 invol...

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Detalles Bibliográficos
Autores principales: Negka, Lydia, Spathoulas, Georgios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587018/
https://www.ncbi.nlm.nih.gov/pubmed/34770287
http://dx.doi.org/10.3390/s21216981
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author Negka, Lydia
Spathoulas, Georgios
author_facet Negka, Lydia
Spathoulas, Georgios
author_sort Negka, Lydia
collection PubMed
description 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.
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spelling pubmed-85870182021-11-13 Towards Secure, Decentralised, and Privacy Friendly Forensic Analysis of Vehicular Data Negka, Lydia Spathoulas, Georgios Sensors (Basel) Article 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. MDPI 2021-10-21 /pmc/articles/PMC8587018/ /pubmed/34770287 http://dx.doi.org/10.3390/s21216981 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Negka, Lydia
Spathoulas, Georgios
Towards Secure, Decentralised, and Privacy Friendly Forensic Analysis of Vehicular Data
title Towards Secure, Decentralised, and Privacy Friendly Forensic Analysis of Vehicular Data
title_full Towards Secure, Decentralised, and Privacy Friendly Forensic Analysis of Vehicular Data
title_fullStr Towards Secure, Decentralised, and Privacy Friendly Forensic Analysis of Vehicular Data
title_full_unstemmed Towards Secure, Decentralised, and Privacy Friendly Forensic Analysis of Vehicular Data
title_short Towards Secure, Decentralised, and Privacy Friendly Forensic Analysis of Vehicular Data
title_sort towards secure, decentralised, and privacy friendly forensic analysis of vehicular data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587018/
https://www.ncbi.nlm.nih.gov/pubmed/34770287
http://dx.doi.org/10.3390/s21216981
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