Privacy-aware distributed mobility data analytics (Contributo in atti di convegno)

Type

• Prodotto della ricerca (Classe)
• Contributo in atti di convegno (Classe)

Label

• Privacy-aware distributed mobility data analytics (Contributo in atti di convegno) (literal)

Anno

• 2013-01-01T00:00:00+01:00 (literal)

Alternative label

• Pratesi F., Monreale A., Wang H., Rinzivillo S., Pedreschi D., Andrienko G., Andrienko N. (2013)
  Privacy-aware distributed mobility data analytics
  in SEBD 2013 - 21st Italian Symposium on Advanced Database Systems, Roccella Jonica, Reggio Calabria, Italy, 30 June - 3 July 2013
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Pratesi F., Monreale A., Wang H., Rinzivillo S., Pedreschi D., Andrienko G., Andrienko N. (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni

• grant agreement 255951 (literal)

Note

• Scopu (literal)
• PuMa (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• CNR-ISTI, Pisa, Italy; Computer Science Department, University of Pisa, Italy; Stevens Institute of Technology, NJ, USA; CNR-ISTI, Pisa, Italy; Computer Science Department, University of Pisa, Italy; Fraunhofer Institute for Intelligent Analysis and Information Systems, Germany; Fraunhofer IAIS Intelligent Analysis and Information Systems, Sankt Augustin University, Bonn, Germany (literal)

Titolo

• Privacy-aware distributed mobility data analytics (literal)

Abstract

• We propose an approach to preserve privacy in an analytical process- ing within a distributed setting, and tackle the problem of obtaining aggregated information about vehicle traffic in a city from movement data collected by in- dividual 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 pri- vacy model and on sketching techniques for efficient data compression, provides a formal data protection safeguard. Using real-life data, we demonstrate the ef- fectiveness of our approach also in terms of data utility preserved by the data transformation. (literal)

Prodotto di

• Knowledge Discovery and Data Mining (ICT.P08.009.003) (Modulo)
• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)

Autore CNR

• Francesca Pratesi (Persona)
• SALVATORE RINZIVILLO (Persona)

Insieme di parole chiave

• Keywords of "Privacy-aware distributed mobility data analytics" (Insieme di parole chiave)

Incoming links:

Prodotto

• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)
• Knowledge Discovery and Data Mining (ICT.P08.009.003) (Modulo)

Autore CNR di

• Francesca Pratesi (Persona)
• SALVATORE RINZIVILLO (Persona)

Insieme di parole chiave di

• Keywords of "Privacy-aware distributed mobility data analytics" (Insieme di parole chiave)