The increasing availability of location and mobility data enables a number of applications, e.g., enhanced navigation services and parking, context-based recommendations, or waiting time predictions at restaurants, which have great potential to improve the quality of life in modern cities. However, the large-scale collection of location data also raises privacy concerns, as mobility patterns may reveal sensitive attributes about users, e.g., home and work places, lifestyles, or even political or religious inclinations. Service providers, i.e., companies with access to location data, often use aggregation as a privacy-preserving strategy to release these data to third-parties for various analytics tasks. The idea being that, by grouping together users' traces, the data no longer contains information to enable inferences about individuals such as the ones mentioned above, while it can be used to obtain useful insights about the crowds. For instance, Waze constructs aggregate traffic models to improve navigation within cities, while Uber provides aggregate data for urban planning purposes.