Abstract
Mobility data are of fundamental importance for understanding the patterns of human movements, developing analytical services and modeling human dynamics. Unfortunately, mobility data also contain individual sensitive information, making it necessary an accurate privacy risk assessment for the individuals involved. In this paper, we propose a methodology for assessing privacy risk in human mobility data. Given a set of individual and collective mobility features, we define the minimum data format necessary for the computation of each feature and we define a set of possible attacks on these data formats. We perform experiments computing the empirical risk in a real-world mobility dataset, and show how the distributions of the considered mobility features are affected by the removal of individuals with different levels of privacy risk.
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Notes
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In compliance with the new EU General Data Protection Regulation.
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- 3.
The Python code for attacks simulation is available here: https://github.com/pellungrobe/privacy-mobility-lib.
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Funded by the European project SoBigData (Grant Agreement 654024).
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Pellungrini, R., Pappalardo, L., Pratesi, F., Monreale, A. (2018). Analyzing Privacy Risk in Human Mobility Data. In: Mazzara, M., Ober, I., Salaün, G. (eds) Software Technologies: Applications and Foundations. STAF 2018. Lecture Notes in Computer Science(), vol 11176. Springer, Cham. https://doi.org/10.1007/978-3-030-04771-9_10
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