@conference {DBLP:conf/esoric, title = {Pattern-Preserving k-Anonymization of Sequences and its Application to Mobility Data Mining}, booktitle = {PiLBA}, year = {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{\textquoteright} and customers{\textquoteright} behavior. However, this puts the citizen{\textquoteright}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 = {https://air.unimi.it/retrieve/handle/2434/52786/106397/ProceedingsPiLBA08.pdf$\#$page=44}, author = {Ruggero G. Pensa and Anna Monreale and Fabio Pinelli and Dino Pedreschi} }