People-centric sensing using people's smartphones offers new research opportunities for large case studies. It presents many challenges, e.g., efficient capture of person's mobility, understanding of context changes and preservation of user privacy. We propose an accurate and energy-efficient method able to capture user's mobility, thus the context changes, while preserving his/her privacy. Our solution can be applied to systems that aim to efficiently sense context on smartphones to study large scale phenomena or perform location management.