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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8587018 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT negkalydia towardssecuredecentralisedandprivacyfriendlyforensicanalysisofvehiculardata AT spathoulasgeorgios towardssecuredecentralisedandprivacyfriendlyforensicanalysisofvehiculardata |