Contributo in atti di convegno, 2017, ENG, 10.1007/978-3-319-66284-8_35
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; CNR-ISTI, Pisa, Italy - Department of Computer Science, University of Pisa, Pisa, Italy; Department of Computer Science, University of Pisa, Pisa, Italy
Mobility data are an important proxy to understand the patterns of human movements, develop analytical services and design models for simulation and prediction of human dynamics. Unfortunately mobility data are also very sensitive, since they may contain personal information 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 computational 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 effectiveness of our approach by experimentation on a real-world GPS dataset and provide a comparison with traditional methods.
SAFECOMP 2017 - International Conference on Computer Safety, Reliability, and Security, pp. 415–426, Trento, Italy, 12 September 2017
Data mining, Privacy, Human mobility
Monreale Anna, Pratesi Francesca, Pellungrini Roberto, Pappalardo Luca
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 385735
Year: 2017
Type: Contributo in atti di convegno
Creation: 2018-03-27 10:20:45.000
Last update: 2018-04-13 18:54:30.000
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1007/978-3-319-66284-8_35
URL: https://link.springer.com/chapter/10.1007%2F978-3-319-66284-8_35
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:385735
DOI: 10.1007/978-3-319-66284-8_35
Scopus: 2-s2.0-85029544230