Predicting and explaining privacy risk exposure in mobility data

Mobility data is a proxy of different social dynamics and its analysis enables a wide range of user services. Unfortunately, mobility data are very sensitive because the sharing of people's whereabouts may arise serious privacy concerns. Existing frameworks for privacy risk assessment provide tools to identify and measure privacy risks, but they often (i) have high computational complexity; and (ii) are not able to provide users with a justification of the reported risks. In this paper, we propose expert, a new framework for the prediction and explanation of privacy risk on mobility data. We empirically evaluate privacy risk on real data, simulating a privacy attack with a state-of-the-art privacy risk assessment framework. We then extract individual mobility profiles from the data for predicting their risk. We compare the performance of several machine learning algorithms in order to identify the best approach for our task. Finally, we show how it is possible to explain privacy risk prediction on real data, using two algorithms: Shap, a feature importance-based method and Lore, a rule-based method. Overall, expert is able to provide a user with the privacy risk and an explanation of the risk itself. The experiments show excellent performance for the prediction task.