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.

Trust Modelling and Management (TMM) techniques are frequently applied in ad-hoc DistributedWireless Networks (DWNs) to stimulate and improve cooperation between network nodes.TMM facilitate DWNs in building a trust network that assures reliability of communication channels, offers an additional layer of security, and enables group decision making processes. Likewise, TMM became in the past decades an attractive solution for solving problems in cooperative ad hoc DWN. The proposed solutions focus on modelling trust in a social-centric approach to maintain a system where nodes trust each other, and detect untrustworthy (malicious) neighbours. The work at hand considers a time span of three years, from 2020 to 2022, where the scientific research in the domain of TMM and DWN is analysed. Our survey aggregates over 130 research papers and investigates the quality of experimental assessment done by each work. Additionally, we establish an indication on the level of experimental analysis done by each study from a TMM security perspective. Lastly, the survey offers a trust ontology, a general overview of a trust models, together with a concise description of trust threats to facilitate the reader’s understanding of TMM.

Connected Automated Vehicles (CAVs) represent a transformative shift in transportation, offering enhanced safety, and efficiency. However, achieving full automation at levels four and five of the Society of Automotive Engineering (SAE) scale poses significant cybersecurity and privacy risks. To address these risks, United Nations Economic Commission for Europe (UNECE) regulations and ISO/SAE 21434 mandate Threat Analysis and Risk Assessment (TARA) as a core methodology for cyber risk management. Existing TARA frameworks, designed for conventional vehicles, fall short for higher automation levels, neglecting complexities such as the absence of human control and data-driven decision making concerns. This work, conducted within ULTIMO, a project tackling the CAVs deployment challenges, introduces TARA 2.0, an enhanced framework addressing cybersecurity, privacy, and expert subjectivity in risk assessment. A step-by-step experimental implementation demonstrates its feasibility, compliance with standards, and potential to secure the deployment of fully automated vehicles.