Abstract:
Location privacy in Intelligent Transportation Systems (ITS) is a critical challenge as mobility services increasingly rely on continuous data collection from vehicles and infrastructure. While these systems optimize traffic flow and safety, the sharing of precise location traces exposes users to risks such as unauthorized surveillance and sensitive pattern disclosure. This paper provides an overview of the role of location privacy within ITS, defining its core components—identity, location, and time. A detailed taxonomy of Location Privacy Protection Mechanisms (LPPMs) is presented, including obfuscation, generalization, dummy-based techniques, pseudonyms, and cryptographic transformations. By evaluating the trade-offs between data utility and privacy, the paper highlights the technical strategies necessary to secure future transportation networks while maintaining user autonomy. This work serves as a guide for engineers to integrate privacy-by design into the next generation of smart mobility solutions.