Contributo in atti di convegno, 2009, ENG, 10.1145/1667502.1667510

Movement data anonymity through generalization.

Andrienko G.; Andrienko N.; Giannotti F.; Monreale A.; Pedreschi D.

Fraunhofer IAIS Sankt, Augustin, Germany; Fraunhofer IAIS Sankt, Augustin, Germany; CNR-ISTI, Pisa, Italy; University of Pisa, Pisa, Italy; University of Pisa, Pisa, Italy

In recent years, spatio-temporal and moving objects databases have gained considerable interest, due to the diusion 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.

2nd SIGSPATIAL ACM GIS 2009. International Workshop on Security and Privacy in GIS and LBS, Seattle, Washington, 4-6 November 2009

Keywords

k-anonymity, Privacy, Spatio-temporal, Clustering

CNR authors

Giannotti Fosca

CNR institutes

ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"

ID: 92036

Year: 2009

Type: Contributo in atti di convegno

Creation: 2010-12-20 00:00:00.000

Last update: 2018-02-16 13:45:27.000

CNR authors

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:92036

DOI: 10.1145/1667502.1667510