Data collected through mobile sensors on private and commercial devices can give valuable insights into mobility patterns and facilitate applications such as urban planning or traffic forecasting. At the same time, such data can carry immense privacy risks for the data producers. Stop detection approaches can reveal a person’s points of interest (POI) by clustering temporal and spatial features, uncovering private attributes such as home or work addresses. Privacy-preserving mechanisms aim at hiding these POIs, for example via speed smoothing approaches that are able to preserve high data utility. We show experimentally on two real-world data sets that trajectories can contain anomalies that are contained to a certain extent when smoothing a route and are not detected by state of the art stop detection algorithms. We propose a novel attack D-TOUR that reveals POIs based on deviations from the optimal route. Our experiments suggest that our proposed attack has similar performance on unprotected data but outperforms the baseline approach, especially when protection is higher and route features become more sparse.