01995nas a2200157   4500008003900000245005100039210005100090300001400141490000700155520154700162100001901709700002001728700002201748700001901770856004801789       2014                          d00aAnonymity preserving sequential pattern mining0 aAnonymity preserving sequential pattern mining  a141–1730 v223 aThe increasing availability of personal data of a sequential nature, such as time-stamped transaction or location data, enables increasingly sophisticated sequential pattern mining techniques. However, privacy is at risk if it is possible to reconstruct the identity of individuals from sequential data. Therefore, it is important to develop privacy-preserving techniques that support publishing of really anonymous data, without altering the analysis results significantly. In this paper we propose to apply the Privacy-by-design paradigm for designing a technological framework to counter the threats of undesirable, unlawful effects of privacy violation on sequence data, without obstructing the knowledge discovery opportunities of data mining technologies. First, we introduce a k-anonymity framework for sequence data, by defining the sequence linking attack model and its associated countermeasure, a k-anonymity notion for sequence datasets, which provides a formal protection against the attack. Second, we instantiate this framework and provide a specific method for constructing the k-anonymous version of a sequence dataset, which preserves the results of sequential pattern mining, together with several basic statistics and other analytical properties of the original data, including the clustering structure. A comprehensive experimental study on realistic datasets of process-logs, web-logs and GPS tracks is carried out, which empirically shows how, in our proposed method, the protection of privacy meets analytical utility.1 aMonreale, Anna1 aPedreschi, Dino1 aPensa, Ruggero, G1 aPinelli, Fabio  uhttp://dx.doi.org/10.1007/s10506-014-9154-600453nas a2200145   4500008003900000245004500039210004500084250000700129260001900136100002200155700001900177700001900196700002000215856007200235       2009                          d00aAnonymous Sequences from Trajectory Data0 aAnonymous Sequences from Trajectory Data  a17  aCamogli, Italy1 aPensa, Ruggero, G1 aMonreale, Anna1 aPinelli, Fabio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/anonymous-sequences-trajectory-data00527nas a2200121   4500008003900000245009700039210006900136300001200205100002000217700002100237700002200258856012500280       2009                          d00aSocial Network Analysis as Knowledge Discovery Process: A Case Study on Digital Bibliography0 aSocial Network Analysis as Knowledge Discovery Process A Case St  a279-2831 aCoscia, Michele1 aGiannotti, Fosca1 aPensa, Ruggero, G  uhttps://kdd.isti.cnr.it/publications/social-network-analysis-knowledge-discovery-process-case-study-digital-bibliography00529nas a2200121   4500008003900000245009700039210006900136300001200205100002000217700002100237700002200258856012700280       2009                          d00aSocial Network Analysis as Knowledge Discovery Process: A Case Study on Digital Bibliography0 aSocial Network Analysis as Knowledge Discovery Process A Case St  a279-2831 aCoscia, Michele1 aGiannotti, Fosca1 aPensa, Ruggero, G  uhttps://kdd.isti.cnr.it/publications/social-network-analysis-knowledge-discovery-process-case-study-digital-bibliography-001351nas 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