• Frequency Vector
• Trajectories

Mobility data are a fundamental source of information for studying human behavior and developing new services for users. In recent years, with the growing presence of smartphones in our lives and the increasing connectivity of devices used everyday, data regarding the whereabouts of individuals in time has become more and more available. However, the exchange and publication of such data may lead to dangerous privacy violations for the people involved. Malicious third parties may try and succeed in identifying individuals even in a deidentified dataset, by attacking in various ways the published data. In this work, we propose a study of the safeness of a common data framework for trajectories, in order to understand the levels of risk for the people involved. We will introduce and develop a simulation of a number of different types of attack and we will apply them to a real mobility dataset. We will then try to understand if such levels of risk can impair the safe use of the data themselves. We will also evaluate how the quality of the most used mobility measures changes by eliminating data of individuals with high privacy risk.