Pattern-Preserving k-Anonymization of Sequences and its Application to Mobility Data Mining
Ruggero G. Pensa, Anna Monreale, Fabio Pinelli, Dino Pedreschi · Institutional Research Information System University of Turin (University of Turin) · 2008
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. 1