Movement data anonymity through generalization (Articolo in rivista)

Type

• Articolo in rivista (Classe)
• Prodotto della ricerca (Classe)

Label

• Movement data anonymity through generalization (Articolo in rivista) (literal)

Anno

• 2010-01-01T00:00:00+01:00 (literal)

Alternative label

• Monreale A.; Andrienko G.; Andrienko N.; Giannotti F.; Pedreschi D.; Rinzivillo S.; Wrobel S. (2010)
  Movement data anonymity through generalization
  in Transactions on data privacy
  (literal)

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

• Monreale A.; Andrienko G.; Andrienko N.; Giannotti F.; Pedreschi D.; Rinzivillo S.; Wrobel S. (literal)

Pagina inizio

• 91 (literal)

Pagina fine

• 121 (literal)

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

• http://www.tdp.cat/issues/abs.a045a10.php (literal)

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

• 3 (literal)

Rivista

• Transactions on data privacy (Rivista)

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

• In: Transactions on Data Privacy, vol. 3 (2) pp. 91 - 121. IIIA-CSIC, 2010. (literal)

Note

• Scopu (literal)

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

• CNR-ISTI, Pisa, Fraunhofer IAIS Sankt, Augustin, Germany, Dipartimento di Informatica, Università di Pisa (literal)

Titolo

• Movement data anonymity through generalization (literal)

Abstract

• Wireless networks and mobile devices, such as mobile phones and GPS receivers, sense and track the movements of people and vehicles, producing society-wide mobility databases. This is a challenging scenario for data analysis and mining. On the one hand, exciting opportunities arise out of discovering new knowledge about human mobile behavior, and thus fuel intelligent info-mobility applications. On other hand, new privacy concerns arise when mobility data are published. The risk is particularly high for GPS trajectories, which represent movement of a very high precision and spatio-temporal resolution: the de-identification of such trajectories (i.e., forgetting the ID of their associated owners) is only a weak protection, as generally it is possible to re-identify a person by ob- serving her routine movements. In this paper we propose a method for achieving true anonymity in a dataset of published trajectories, by defining a transformation of the original GPS trajectories based on spatial generalization and k-anonymity. The proposed method offers a formal data protection safeguard, quantified as a theoretical upper bound to the probability of re-identification. We conduct a thorough study on a real-life GPS trajectory dataset, and provide strong empirical evidence that the proposed anonymity techniques achieve the conflicting goals of data utility and data privacy. In practice, the achieved anonymity protection is much stronger than the theoretical worst case, while the quality of the cluster analysis on the trajectory data is preserved. (literal)

Prodotto di

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

Autore CNR

• Anna Monreale (Persona)
• FOSCA GIANNOTTI (Unità di personale interno)
• SALVATORE RINZIVILLO (Persona)
• DINO PEDRESCHI (Persona)

Insieme di parole chiave

• Keywords of "Movement data anonymity through generalization" (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

• FOSCA GIANNOTTI (Unità di personale interno)
• Anna Monreale (Persona)
• DINO PEDRESCHI (Persona)
• SALVATORE RINZIVILLO (Persona)

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

• Transactions on data privacy (Rivista)

Insieme di parole chiave di

• Keywords of "Movement data anonymity through generalization" (Insieme di parole chiave)