Movement data anonymity through generalization. (Contributo in atti di convegno)

Type

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

Label

• Movement data anonymity through generalization. (Contributo in atti di convegno) (literal)

Anno

• 2009-01-01T00:00:00+01:00 (literal)

Alternative label

• Andrienko G., Andrienko N., Giannotti F., Monreale A., Pedreschi D. (2009)
  Movement data anonymity through generalization.
  in 2nd SIGSPATIAL ACM GIS 2009. International Workshop on Security and Privacy in GIS and LBS, Seattle, Washington
  (literal)

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

• Andrienko G., Andrienko N., Giannotti F., Monreale A., Pedreschi D. (literal)

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

• In: SPRINGL '09 - 2nd SIGSPATIAL ACM GIS 2009. International Workshop on Security and Privacy in GIS and LBS (Seattle, Washington, 4-6 November 2009). Proceedings, pp. 27 - 31. ACM, 2009. (literal)

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

• In recent years, spatio-temporal and moving objects databases have gained considerable interest, due to the di usion of mobile devices (e.g., mobile phones, RFID devices and GPS devices) and of new applications, where the discovery of consumable, concise, and applicable knowledge is the key step. Clearly, in these applications privacy is a concern,since models extracted from this kind of data can reveal the behavior of group of individuals, thus compromising their privacy. Movement data present a new challenge for the privacy-preserving data mining community because of their spatial and temporal characteristics. In this position paper we brie y present an approach for the generalization of movement data that can be adopted for obtaining k-anonymity in spatio-temporal datasets; specif- ically, it can be used to realize a framework for publishing of spatio-temporal data while preserving privacy. We ran a preliminary set of experiments on a real-world trajectory dataset, demonstrating that this method of generalization of trajectories preserves the clustering analysis results. (literal)

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

• CNR-ISTI, Pisa, Fraunhofer IAIS Sankt, Augustin, Germany , University of Pisa (literal)

Titolo

• Movement data anonymity through generalization. (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

• FOSCA GIANNOTTI (Unità di personale interno)

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)

Insieme di parole chiave di

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