Privacy-aware distributed mobility data analytics

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

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

4 Scopus citations

Abstract

We propose an approach to preserve privacy in an 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 they may describe typical movement behaviors and therefore be used for 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 publication21st Italian Symposium on Advanced Database Systems, SEBD 2013
Pages329-342
Number of pages14
StatePublished - 2013
Event21st Italian Symposium on Advanced Database Systems, SEBD 2013 - Roccella Jonica, Reggio Calabria, Italy
Duration: 30 Jun 20134 Jul 2013

Publication series

Name21st Italian Symposium on Advanced Database Systems, SEBD 2013

Conference

Conference21st Italian Symposium on Advanced Database Systems, SEBD 2013
Country/TerritoryItaly
CityRoccella Jonica, Reggio Calabria
Period30/06/134/07/13

Fingerprint

Dive into the research topics of 'Privacy-aware distributed mobility data analytics'. Together they form a unique fingerprint.

Cite this