Pattern-preserving k-anonymization of sequences and its application to mobility data mining
Pensa R. G., Monreale A., Pinelli F., Pedreschi D.
k-anonymity Privacy-preserving data mining Sequential patternsi
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.
Source: The 1st International Workshop on Privacy in Location-Based Applications, pp. 44–60, Malaga, Spain, 9 ottobre 2008
Publisher: CEUR-WS.org, Aachen, DEU
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@inproceedings{oai:it.cnr:prodotti:91876,
title = {Pattern-preserving k-anonymization of sequences and its application to mobility data mining},
author = {Pensa R. G. and Monreale A. and Pinelli F. and Pedreschi D.},
publisher = {CEUR-WS.org, Aachen, DEU},
booktitle = {The 1st International Workshop on Privacy in Location-Based Applications, pp. 44–60, Malaga, Spain, 9 ottobre 2008},
year = {2008}
}
0000-0001-8541-0284