Due to the emergence of geolocated applications, more and more mobility traces are generated on a daily basis and collected in the form of geolocated datasets. If an unauthorized entity can access this data, it can use it to infer personal information about the individuals whose movements are contained within these datasets, such as learning their home and place of work or even their social network, thus causing a privacy breach. In order to protect the privacy of individuals, a sanitization process, which adds uncertainty to the data and removes some sensitive information, has to be performed. The global objective of GEPETO (for GEoPrivacy Enhancing TOolkit) is to provide researchers concerned with geo-privacy with means to evaluate various sanitization techniques and inference attacks on geolocated data. We describe our experiments conducted with GEPETO for comparing different inference attacks, and evaluating their efficiency for the identification of point of interests, as well as their resilience to sanitization mechanisms such as sampling and perturbation. We also introduce a mobility model that we coin as mobility Markov Chain, which can represent in a compact yet precise way the mobility behaviour of an individual. Finally, we describe an algorithm for learning such a structure from the mobility traces of an individual and we report on experimentations performed with real mobility data.