Privacy Challenges With Protecting Live Vehicular Location Context

Bradbury, Matthew; Taylor, Phillip; Atmaca, Ugur Ilker; Maple, Carsten; Griffiths, Nathan

doi:10.1109/access.2020.3038533

Cited by 8 publications

(3 citation statements)

References 80 publications

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“…Due to the increasing integration of information and communication technology into vehicles, privacy is becoming more and more important [23]. Pervasive, automatic tracking of vehicles is possible, with the resulting threats to individual privacy [5]. [34] lists privacy issues for electric mobility and [20] privacy challenges in EV charging, including concealed charge data aggregation, charge session unlinkability, identity-/location privacy, and privacy-preserving billing.…”

Section: Related Workmentioning

confidence: 99%

Integrating Privacy into the Electric Vehicle Charging Architecture

Kern

Lauser

Krauß

2022

PoPETs

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The Electric Vehicle (EV) charging architecture consists of several actors which communicate with different protocols. A serious issue is the lack of adequate privacy-preserving measures that enables the generation of movement profiles or inferring consumer habits by all of the involved actors. In this paper, we propose an extension of a Trusted Platform Module (TPM)-based Direct Anonymous Attestation (DAA) scheme to enable privacy-preserving charging authorization and billing. Our implementation shows that our solution can be easily integrated into existing protocols of the Plug-and-Charge (PnC) EV charging architecture and introduces only minor overhead. The formal analysis using the Tamarin prover shows the security and privacy of our protocol extension.

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Section: Related Workmentioning

confidence: 99%

Integrating Privacy into the Electric Vehicle Charging Architecture

Kern

Lauser

Krauß

2022

PoPETs

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“…Therefore, researching more sustainable solutions to generate traffic self-warning systems in urban areas is urgent. Probe data, such as Global Positioning System (GPS) data, Sustainability 2024, 16, 2453 2 of 21 can be used to forecast average vehicle speed in early warning systems for sustainable development in transportation management, which requires more in-depth research to analyze these data to support forecasting vehicle speeds and traffic status in urban areas.…”

Section: Introductionmentioning

confidence: 99%

“…The valuable data source from journey monitoring data collected by GPS signals mounted on vehicles only serves the purpose of managing or handling penalty situations due to commuter violations of traffic laws. Additionally, it can be exploited by a third party, such as Google Maps and other mobile applications, for different purposes [16]. This limits the development of new domestic application platforms to serve traffic optimization in each country and further concerns information security issues [17].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Deep Neural Networks for Traffic Speed Forecasting Regarding Sustainable Traffic Management Using Probe Data from Registered Transport Vehicles on Multilane Roads

Do,

Tran,

et al. 2024

Sustainability

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Early forecasting of vehicle flow speeds is crucial for sustainable traffic development and establishing Traffic Speed Forecasting (TSF) systems for each country. While online mapping services offer significant benefits, dependence on them hampers the development of domestic alternative platforms, impeding sustainable traffic management and posing security risks. There is an urgent need for research to explore sustainable solutions, such as leveraging Global Positioning System (GPS) probe data, to support transportation management in urban areas effectively. Despite their vast potential, GPS probe data often present challenges, particularly in urban areas, including interference signals and missing data. This paper addresses these challenges by proposing a process for handling anomalous and missing GPS signals from probe vehicles on parallel multilane roads in Vietnam. Additionally, the paper investigates the effectiveness of techniques such as Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) and Genetic Algorithm Long Short-Term Memory (GA-LSTM) in enhancing LSTM networks for TSF using GPS data. Through empirical analysis, this paper demonstrates the efficacy of PSO-LSTM and GA-LSTM compared to existing methods and the state-of-the-art LSTM approach. Performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Median Absolute Error (MDAE) validate the proposed models, providing insights into their forecasting accuracy. The paper also offers a comprehensive process for handling GPS outlier data and applying GA and PSO algorithms to enhance LSTM network quality in TSF, enabling researchers to streamline calculations and improve supposed model efficiency in similar contexts.

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Evaluating Consumer Understanding and Awareness of Connected and Autonomous Vehicle Data Privacy

Barber

Furnell

2023

Communications in Computer and Information Science

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Privacy Challenges With Protecting Live Vehicular Location Context

Cited by 8 publications

References 80 publications

Integrating Privacy into the Electric Vehicle Charging Architecture

Integrating Privacy into the Electric Vehicle Charging Architecture

Enhanced Deep Neural Networks for Traffic Speed Forecasting Regarding Sustainable Traffic Management Using Probe Data from Registered Transport Vehicles on Multilane Roads

Evaluating Consumer Understanding and Awareness of Connected and Autonomous Vehicle Data Privacy

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