PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Andrienko G., Andrienko N., Giannotti F., Monreale A., Pedreschi D. Movement data anonymity through generalization. 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.
 
 
Abstract
(English)
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.
URL: http://portal.acm.org/citation.cfm?id=1667502.1667510&coll=DL&dl=GUIDE&CFID=2229136&CFTOKEN=15070793
DOI: 10.1145/1667502.1667510
Subject k-anonymity
Privacy
Spatio-temporal
Clustering
H.2.8 Database Applications. Spatial databases and GIS
K.4.1 Public Policy Issues. Privacy


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