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dc.contributor.authorMegias, David-
dc.contributor.authorAlatrista Salas, Hugo-
dc.contributor.authorSalas, Julián-
dc.contributor.authorMaehara Aliaga, Yoshitomi-
dc.contributor.authorNúñez del Prado, Miguel-
dc.contributor.otherUniversitat Oberta de Catalunya-
dc.contributor.otherPontificia Universidad Católica del Perú-
dc.contributor.otherPeru Research Development, and Innovation-
dc.contributor.otherUniversidad Andina del Cusco-
dc.contributor.otherUniversitat Rovira i Virgili (URV)-
dc.identifier.citationNunez-del-Prado, M., Maehara-Aliaga, Y., Salas, J., Alatrista-Salas, H., & Megías, D. (2022). A Graph-Based Differentially Private Algorithm for Mining Frequent Sequential Patterns. Applied Sciences, 12(4), 2131. MDPI AG. Retrieved from
dc.description.abstractCurrently, individuals leave a digital trace of their activities when they use their smartphones, social media, mobile apps, credit card payments, Internet surfing profile, etc. These digital activities hide intrinsic usage patterns, which can be extracted using sequential pattern algorithms. Sequential pattern mining is a promising approach for discovering temporal regularities in huge and heterogeneous databases. These sequences represent individuals¿ common behavior and could contain sensitive information. Thus, sequential patterns should be sanitized to preserve individuals¿ privacy. Hence, many algorithms have been proposed to accomplish this task. However, these techniques add noise to the candidate support before they are validated as, frequently, and thus, they cannot be applied without having access to all the users¿ sequences data. In this paper, we propose a differential privacy graph-based technique for publishing frequent sequential patterns. It is applied at the post-processing stage; hence it may be used to protect frequent sequential patterns after they have been extracted, without the need to access all the users¿ sequences. To validate our proposal, we performed a detailed assessment of its utility as a pattern mining algorithm and calculated the impact of the sanitization mechanism on a recommender system. We further evaluated its information loss disclosure risk and performed a comparison with the DP-FSM algorithm.en
dc.publisherApplied Sciences-
dc.relation.ispartofApplied Sciences, 2022, 12(4).-
dc.rightsCC BY-
dc.subjectsequential pattern miningen
dc.subjectdifferential privacyen
dc.subjectfrequent pattern miningen
dc.subjectedge differential privacyen
dc.subjectgraph differential privacyen
dc.subjectanonymization of big dataen
dc.subjectanonimización del big dataes
dc.subjectanonimització del big dataca
dc.subjectminería de patrones secuencialeses
dc.subjectmineria de patrons seqüencialsca
dc.subjectprivacidad diferenciales
dc.subjectprivacitat diferencialca
dc.subjectminería de patrones frecuenteses
dc.subjectmineria de patrons freqüentsca
dc.subjectgráfico de privacidad diferenciales
dc.subjectgràfic de privacitat diferencialca
dc.subject.lcshdata miningen
dc.titleA graph-based differentially private algorithm for mining frequent sequential patterns-
dc.subject.lemacmineria de dadesca
dc.subject.lcshesdata mininges
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