WEVAN – A mechanism for evidence creation and verification in VANETs

Fuentes, José María de; González-Manzano, Lorena; González-Tablas, Ana I.; Blasco, Jorge

doi:10.1016/j.sysarc.2013.07.009

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…How this evidence is securely stored is out of the scope of their work. In [8] authors make the collection of evidence and witnesses the focus of their work, but only from the vehicle point of view (requester) and functionally oriented to demonstrate vehicle's facts, like for instance when in need for challenging a driving fine.…”

Section: Security Challenges In Vehicular Networkmentioning

confidence: 99%

Edge-Assisted Vehicular Networks Security

Onieva

Rı́os

Román

et al. 2019

IEEE Internet Things J.

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Edge Computing paradigms are expected to solve some major problems affecting current application scenarios that rely on Cloud computing resources to operate. These novel paradigms will bring computational resources closer to the users and by doing so they will not only reduce network latency and bandwidth utilization but will also introduce some attractive context-awareness features to these systems. In this paper we show how the enticing features introduced by Edge Computing paradigms can be exploited to improve security and privacy in the critical scenario of vehicular networks (VN), especially existing authentication and revocation issues. In particular, we analyze the security challenges in VN and describe three deployment models for vehicular edge computing, which refrain from using vehicular-to-vehicular communications. The result is that the burden imposed to vehicles is considerably reduced without sacrificing the security or functional features expected in vehicular scenarios.

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Section: Security Challenges In Vehicular Networkmentioning

confidence: 99%

Edge-Assisted Vehicular Networks Security

Onieva

Rı́os

Román

et al. 2019

IEEE Internet Things J.

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show abstract

“…This is not erroneous, it just means that they were designed for another purpose. For example, in [ 22 ], the authors propose a solution to acquire digital evidence from vehicles that act as witnesses in a vehicular ad-hoc network (VANET). However, privacy principles are not considered, probably because the cost of an incident in such a critical network is sufficient motivation for the adoption of the approach.…”

Section: Related Workmentioning

confidence: 99%

IoT-Forensics Meets Privacy: Towards Cooperative Digital Investigations

Nieto

Rı́os

López

2018

Sensors

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IoT-Forensics is a novel paradigm for the acquisition of electronic evidence whose operation is conditioned by the peculiarities of the Internet of Things (IoT) context. As a branch of computer forensics, this discipline respects the most basic forensic principles of preservation, traceability, documentation, and authorization. The digital witness approach also promotes such principles in the context of the IoT while allowing personal devices to cooperate in digital investigations by voluntarily providing electronic evidence to the authorities. However, this solution is highly dependent on the willingness of citizens to collaborate and they may be reluctant to do so if the sensitive information within their personal devices is not sufficiently protected when shared with the investigators. In this paper, we provide the digital witness approach with a methodology that enables citizens to share their data with some privacy guarantees. We apply the PRoFIT methodology, originally defined for IoT-Forensics environments, to the digital witness approach in order to unleash its full potential. Finally, we show the feasibility of a PRoFIT-compliant digital witness with two use cases.

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“…Some schemes allocate the escrow functionality in a distributed way to prevent it from corrupted law enforcement authorities [14,15]. By contrast, in other complementary approaches, evidence generation about driver behaviour is based on an alternative data source [16]. Moreover, we have previously explored how to apply ITS technologies for enforcement as to improve the offence description and the offender identification [5].…”

Section: Introductionmentioning

confidence: 99%