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Title

The Interface of Privacy and Data Security in Automated City Shuttles: The GDPR Analysis

Authors
Kechagia, Sotiria
Published in Applied sciences. 2022, vol. 12, no. 9, 4413
Abstract The fast evolution and prevalence of driverless technologies has facilitated the testing and deployment of automated city shuttles (ACSs) as a means of public transportation in smart cities. For their efficient functioning, ACSs require a real-time data compilation and exchange of information with their internal components and external environment. However, that nexus of data exchange comes with privacy concerns and data protection challenges. In particular, the technical realization of stringent data protection laws on data collection and processing are key issues to be tackled within the ACSs ecosystem. Our work provides an in-depth analysis of the GDPR requirements that should be considered by the ACSs’ stakeholders during the collection, storage, use, and transmission of data to and from the vehicles. First, an analysis is performed on the data processing principles, the rights of data subjects, and the subsequent obligations for the data controllers where we highlight the mixed roles that can be assigned to the ACSs stakeholders. Secondly, the compatibility of privacy laws with security technologies focusing on the gap between the legal definitions and the technological implementation of privacy-preserving techniques are discussed. In face of the GDPR pitfalls, our work recommends a further strengthening of the data protection law. The interdisciplinary approach will ensure that the overlapping stakeholder roles and the blurring implementation of data privacy-preserving techniques within the ACSs landscape are efficiently addressed.

Keywords Automated city shuttlesConnected automated vehiclesShared mobilityData privacyPrivacy-preservingGDPR
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European Commission: AVENUE
European Commission: nIoVe
European Commission: SHOW
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BENYAHYA, Meriem et al. The Interface of Privacy and Data Security in Automated City Shuttles: The GDPR Analysis. In: Applied sciences, 2022, vol. 12, n° 9, p. 4413. doi: 10.3390/app12094413 https://archive-ouverte.unige.ch/unige:160863

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Deposited on : 2022-05-16

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