Privacy-preserving distributed movement data aggregation

Anna Monreale, Wendy Hui Wang, Francesca Pratesi, Salvatore Rinzivillo, Dino Pedreschi, Gennady Andrienko, Natalia Andrienko

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

24 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationGeographic Information Science at the Heart of Europe
EditorsBenedicte Bucher, Danny Vandenbroucke, Joep Crompvoets
Pages225-245
Number of pages21
ISBN (Electronic)9783319006147
DOIs
StatePublished - 2013
Event16th AGILE Conference on Geographic Information Science - Leuven, Belgium
Duration: 14 May 201317 May 2013

Publication series

NameLecture Notes in Geoinformation and Cartography
Volume2013-January
ISSN (Print)1863-2351

Conference

Conference16th AGILE Conference on Geographic Information Science
Country/TerritoryBelgium
CityLeuven
Period14/05/1317/05/13

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