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Istituto di Scienza e Tecnologie dell'Informazione     
Pensa R. G., Monreale A., Pinelli F., Pedreschi D. Pattern-preserving k-anonymization of sequences and its application to mobility data mining. In: PiLBA 2008 - The 1st International Workshop on Privacy in Location-Based Applications (Malaga, Spain, 9 ottobre 2008). Proceedings, vol. 397 pp. 44 - 60. CEUR-WS.org, 2008.
 
 
Abstract
(English)
Sequential pattern mining is a major research field in knowledge discovery and data mining. Thanks to the increasing availability of transaction data, it is now possible to provide new and improved services based on users' and customers' behavior. However, this puts the citizen's privacy at risk. Thus, it is important to develop new privacy-preserving data mining techniques that do not alter the analysis results significantly. In this paper we propose a new approach for anonymizing sequential data by hiding infrequent, and thus potentially sensible, subsequences. Our approach guarantees that the disclosed data are k-anonymous and preserve the quality of extracted patterns. An application to a real-world moving object database is presented, which shows the effectiveness of our approach also in complex contexts.
URL: http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-397/
Subject k-anonymity
privacy-preserving data mining
sequential patternsi
H.2.8 Database Applications


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