Understanding users’ mobility from sensor data such as Global Positioning System (GPS) logs can allow for applications to gain insight and as a result provide contextual information back to users. Nowadays, prevalent applications of such methods can be observed. For example, one of which can be seen in social media, through the increased use of geotagging. The upsurge in such practices can be explained through the fact that, today, roughly half of the world’s population owns a smartphone device. As a result, a substantial amount of people carry with them numerous sensors on a daily basis. This gives rise to the generation of exponential amounts of data from which thorough insights can be gained on individuals. Consequently, this highlights the need for the aforementioned applications to be compatible with privacy-enhancing technologies (PET), in order to maintain a certain level of their practicality while progressively protecting the affected users’ privacy. The objective of this semester project is to develop a post-processing technique that leverages individuals’ GPS sensor readings in order to understand users’ mobility patterns, with a specific focus on public transportation. Subsequently, we evaluate the impact of various GPS related PETs on the algorithm’s performance to get an understanding of the results’ usability.