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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Autores principales:	Lydia Negka, Georgios Spathoulas
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	forensics vehicular blockchain integrity automation privacy Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/6d145f23cdee48d19f67e55f459b9d8c
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id	oai:doaj.org-article:6d145f23cdee48d19f67e55f459b9d8c
record_format	dspace
spelling	oai:doaj.org-article:6d145f23cdee48d19f67e55f459b9d8c2021-11-11T19:01:37ZTowards Secure, Decentralised, and Privacy Friendly Forensic Analysis of Vehicular Data10.3390/s212169811424-8220https://doaj.org/article/6d145f23cdee48d19f67e55f459b9d8c2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/6981https://doaj.org/toc/1424-8220The 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.Lydia NegkaGeorgios SpathoulasMDPI AGarticleforensicsvehicularblockchainintegrityautomationprivacyChemical technologyTP1-1185ENSensors, Vol 21, Iss 6981, p 6981 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	forensics vehicular blockchain integrity automation privacy Chemical technology TP1-1185
spellingShingle	forensics vehicular blockchain integrity automation privacy Chemical technology TP1-1185 Lydia Negka Georgios Spathoulas Towards Secure, Decentralised, and Privacy Friendly Forensic Analysis of Vehicular Data
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.
format	article
author	Lydia Negka Georgios Spathoulas
author_facet	Lydia Negka Georgios Spathoulas
author_sort	Lydia Negka
title	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_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_sort	towards secure, decentralised, and privacy friendly forensic analysis of vehicular data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6d145f23cdee48d19f67e55f459b9d8c
work_keys_str_mv	AT lydianegka towardssecuredecentralisedandprivacyfriendlyforensicanalysisofvehiculardata AT georgiosspathoulas towardssecuredecentralisedandprivacyfriendlyforensicanalysisofvehiculardata
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Towards Secure, Decentralised, and Privacy Friendly Forensic Analysis of Vehicular Data

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