The desire to share one’s location with friends and family or to use location information for navigation and recommendations services is often overshadowed by the need to preserve privacy. As recent progress in big data analytics, ambient intelligence, and conflation techniques is met with the economy’s growing hunger for data, even formerly negligible digital footprints become revealing of our activities. The majority of established geoprivacy research tries to protect an individual’s location by different masking or perturbation techniques or by suppressing and generalizing an individual’s characteristics to a degree where she cannot be singled out from a crowd. In this work we demonstrate that location privacy may already be compromised before these techniques take effect. More concretely, we discuss how everyday digital footprints such as timestamps, geosocial check-ins, and short social media messages, e.g., tweets, are indicative of the user’s location. We focus particularly on places and highlight how protecting place-based information differs from a purely spatial perspective. The presented research is based on so-called semantic signatures that are mined from millions of geosocial checkins and enable a probabilistic framework on the level of geographic feature types, here Points Of Interest (POI). While our work is compatible with leading privacy techniques, we take a user-centric perspective and illustrate how privacy-enabled services could guide the users by increasing information entropy.