Fast estimation of privacy risk in human mobility data Roberto Pellungrini 1 , Luca Pappalardo 1,2 , Francesca Pratesi 1,2 , and Anna Monreale 1 1 Department of Computer Science, University of Pisa, Italy 2 ISTI-CNR, Pisa, Italy Abstract. Mobility data are an important proxy to understand the pat- terns of human movements, develop analytical services and design models for simulation and prediction of human dynamics. Unfortunately mobil- ity data are also very sensitive, since they may contain personal informa- tion about the individuals involved. Existing frameworks for privacy risk assessment enable the data providers to quantify and mitigate privacy risks, but they suffer two main limitations: (i) they have a high compu- tational complexity; (ii) the privacy risk must be re-computed for each new set of individuals, geographic areas or time windows. In this paper we explore a fast and flexible solution to estimate privacy risk in human mobility data, using predictive models to capture the relation between an individual’s mobility patterns and her privacy risk. We show the effec- tiveness of our approach by experimentation on a real-world GPS dataset and provide a comparison with traditional methods. 1 Introduction In the last years human mobility analysis has attracted a growing interest due to its importance in several applications such as urban planning, transportation en- gineering and public health (11). The great availability of these data has offered the opportunity to observe human movements at large scales and in great detail, leading to the discovery of quantitative patterns (9), the mathematical modeling of human mobility (10; 15) and so on. Unfortunately mobility data are sensitive because they may reveal personal information or allow the re-identification of in- dividuals, creating serious privacy risks if they are analyzed with malicious intent (13). Driven by these sensitive issues, researchers have developed methodologies and frameworks to mitigate the individual privacy risks associated to the study of GPS trajectories and Big Data in general (1). These tools aim at preserving both the right to individual’s privacy and the effectiveness of the analytical re- sults, trying to find a reasonable trade-off between privacy protection and data quality. They also allow the definition of infrastructures for supporting privacy and of technical requirements for data protection, enforcing cross-relations be- tween privacy-preserving solutions and legal regulations, since assessing privacy risk is required by the new EU General Data Protection Regulation. To this aim, Pratesi et al. (12) propose a framework for the privacy risk assessment of