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Towards secure, decentralised, and privacy friendly forensic analysis of vehicular data

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Auteur
Negka L., Spathoulas G.
Date
2021
Language
en
DOI
10.3390/s21216981
Sujet
Accidents
Automotive industry
Blockchain
Data privacy
Multimedia services
Block-chain
Decentralised
Forensic
Forensic analysis
Integrity
Privacy
Simulation
Technological progress
Traffic
Vehicular
Vehicles
car driving
computer simulation
privacy
technology
Automobile Driving
Blockchain
Computer Simulation
Privacy
Technology
MDPI
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Résumé
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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
URI
http://hdl.handle.net/11615/77133
Collections
  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ. [19735]

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