PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Monreale A., Wang W. H., Pratesi F., Rinzivillo S., Pedreschi D., Andrienko G., Andrienko N. Privacy-preserving distributed movement data aggregation. Danny Vandenbroucke, Bénédicte Bucher, Joep Crompvoets (eds.). (Lecture Notes in Geoinformation and Cartography, vol. 2013). Heidelberg: Springer, 2013.
 
 
Abstract
(English)
We propose a novel approach to privacy-preserving analytical processing within a distributed setting, and tackle the problem of obtaining aggregated information about vehicle traffic in a city from movement data collected by individual vehicles and shipped to a central server. Movement data are sensitive because people's whereabouts have the potential to reveal intimate personal traits, such as religious or sexual preferences, and may allow re-identification of individuals in a database. We provide a privacy-preserving framework for movement data aggregation based on trajectory generalization in a distributed environment. The proposed solution, based on the differential privacy model and on sketching techniques for efficient data compression, provides a formal data protection safeguard. Using real-life data, we demonstrate the effectiveness of our approach also in terms of data utility preserved by the data transformation.
URL: http://link.springer.com/chapter/10.1007%2F978-3-319-00615-4_13
DOI: 10.1007/978-3-319-00615-4_13
Subject Privacy
Distributed systems
Mobility
K.4.1 Public Policy Issues. Privacy
H.2.8 Database Applications. Spatial databases and GIS


Icona documento 1) Download Document PDF


Icona documento Open access Icona documento Restricted Icona documento Private

 


Per ulteriori informazioni, contattare: Librarian http://puma.isti.cnr.it

Valid HTML 4.0 Transitional