@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}
}
