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.

Pattern-Preserving k-Anonymization of Sequences and its Application to Mobility Data Mining

PENSA, Ruggero Gaetano;
2008-01-01

Abstract

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.
2008
International Workshop on Privacy in Location-Based Applications PiLBA'08
Malaga, Spain
October 9, 2008
PiLBA '08 Privacy in Location-Based Applications
CEUR-WS.org
397
44
60
k-anonymity; sequential pattern mining; privacy by design
R. G. Pensa; A. Monreale; F. Pinelli; D. Pedreschi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/68392
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