Abstract

Modern vehicles generate vast amounts of data, raising significant privacy concerns while offering substantial utility for applications such as smart city infrastructure, urban mobility management, and environmental monitoring. Privacy-Enhancing Technologies (PETs) provide a way to protect sensitive information while maintaining data usability. This thesis develops an annotation model for PETs in the automotive domain, focusing specifically on location data privacy protection. The model adopts a dual-perspective approach, separately analyzing and annotating use case requirements and PET capabilities. By systematically mapping PETs to relevant use cases, the model facilitates structured comparisons of different PETs, helping users select suitable PETs while balancing privacy protection effectiveness and data utility. The annotation is derived from a systematic literature review following the PRISMA methodology and is implemented to model a selected set of use cases and PETs. By adopting a dual-perspective approach, this work assists users in navigating and comparing PETs to make informed decisions that balance privacy and utility in location-based applications in connected vehicles (CVs).