The rise of mobile technologies and spatial crowdsourcing platforms has led to a proliferation of geolocated data, widely exploited for applications such as traffic management, urban planning, and task assignment. However, this wealth of information brings significant privacy risks for users, particularly due to inference attacks, which aim to extract sensitive information from recurring spatio-temporal patterns. For example, analyzing a user's regular movements can reveal their home address, workplace, or personal habits such as medical visits, religious practices, or union-related activities. Even after anonymization, cross-referencing datasets is often sufficient to reconstruct an accurate user profile. In this talk, we will explore how traditional privacy-preserving techniques (such as pointwise anonymization, noise addition, or dummy location generation) often prove insufficient.