01351nas a2200133   4500008003900000245009600039210006900135520084300204100002201047700001901069700001901088700002001107856009001127       2008                          d00aPattern-Preserving k-Anonymization of Sequences and its Application to Mobility Data Mining0 aPatternPreserving kAnonymization of Sequences and its Applicatio3 aSequential 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 aPensa, Ruggero, G1 aMonreale, Anna1 aPinelli, Fabio1 aPedreschi, Dino  uhttps://air.unimi.it/retrieve/handle/2434/52786/106397/ProceedingsPiLBA08.pdf#page=44