While most mobile applications nowadays fulfil basic privacy-preserving properties, they still leave significant surfaces open for privacy breaches that leverage on subtle features of the collected data. The focus of this proposal is to analyse this problem for trajectory data. Such data is the foundation of Location-Based Services (LBS), which represent a significant portion of today’s most popular mobile services, but may also be collected by applications that offer opt-in geo-referencing or even by the mobile operator itself. While the the user is usually informed at install time that the service will access positioning data, she is given absolutely no information about the frequency with which data is collected and how it is used precisely upon collection — including purposes that go beyond the primary objective of the application. The objective of this project is raising user awareness about the privacy leakage of trajectory data. To this end, we will provide end-users with (i) a clear, intuitive visualizations of the precise spatiotemporal trajectory information gathered by each mobile application, (ii) an equivalent visualization from localization data possibly gathered by the operator from their mobile network activity, and (iii) indirect knowledge that may infer from the trajectory data it gathered by using data mining techniques, including, e.g., home address, employer’s name, commuting patterns, religion, health issues, etc.