Contributo in volume, 2018, ENG, 10.1007/978-3-030-04771-9_10
Pellungrini R.; Pappalardo L.; Pratesi F.; Monreale A.
Department of Computer Science, University of Pisa, Pisa, Italy; CNR-ISTI, Pisa, Italy; Department of Computer Science, University of Pisa, Pisa, Italy e CNR-ISTI, Pisa, Italy; Department of Computer Science, University of Pisa, Pisa, Italy
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.
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 424287
Year: 2018
Type: Contributo in volume
Creation: 2020-06-22 11:34:18.000
Last update: 2020-07-21 12:05:19.000
CNR authors
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1007/978-3-030-04771-9_10
URL: https://link.springer.com/chapter/10.1007/978-3-030-04771-9_10
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:424287
DOI: 10.1007/978-3-030-04771-9_10
Scopus: 2-s2.0-85058523279