TY - GEN
T1 - Privacy-preserving distributed movement data aggregation
AU - Monreale, Anna
AU - Wang, Wendy Hui
AU - Pratesi, Francesca
AU - Rinzivillo, Salvatore
AU - Pedreschi, Dino
AU - Andrienko, Gennady
AU - Andrienko, Natalia
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2013.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84939633427&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84939633427&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-00615-4_13
DO - 10.1007/978-3-319-00615-4_13
M3 - Conference contribution
AN - SCOPUS:84939633427
T3 - Lecture Notes in Geoinformation and Cartography
SP - 225
EP - 245
BT - Geographic Information Science at the Heart of Europe
A2 - Bucher, Benedicte
A2 - Vandenbroucke, Danny
A2 - Crompvoets, Joep
T2 - 16th AGILE Conference on Geographic Information Science
Y2 - 14 May 2013 through 17 May 2013
ER -