Contributo in volume, 2018, ENG, 10.1007/978-3-030-04771-9_10

Analyzing privacy risk in human mobility data

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.

Keywords

Privacy risks, human mobility

CNR authors

Pappalardo Luca

CNR institutes

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 IDs

CNR OAI-PMH: oai:it.cnr:prodotti:424287

DOI: 10.1007/978-3-030-04771-9_10

Scopus: 2-s2.0-85058523279