TY - CONF T1 - Pattern-Preserving k-Anonymization of Sequences and its Application to Mobility Data Mining T2 - PiLBA Y1 - 2008 A1 - Ruggero G. Pensa A1 - Anna Monreale A1 - Fabio Pinelli A1 - Dino Pedreschi AB - 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. JF - PiLBA UR - https://air.unimi.it/retrieve/handle/2434/52786/106397/ProceedingsPiLBA08.pdf#page=44 ER -