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dc.contributor.authorSalas, Julián-
dc.contributor.authorMegias, David-
dc.contributor.authorTorra, Vicenç-
dc.contributor.authorToger, Marina-
dc.contributor.authorDahne, Joel-
dc.contributor.authorSainudiin, Raazesh-
dc.contributor.otherUniversitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)-
dc.contributor.otherMaynooth University-
dc.contributor.otherUniversity of Skövde-
dc.contributor.otherUppsala University-
dc.date.accessioned2020-09-02T12:56:21Z-
dc.date.available2020-09-02T12:56:21Z-
dc.date.issued2020-03-02-
dc.identifier.citationSalas, J., Megías, D., Torra, V., Toger, M., Dahne, J. & Sainudiin, R. (2020). Swapping trajectories with a sufficient sanitizer. Pattern Recognition Letters, 131(), 474-480. doi: 10.1016/j.patrec.2020.02.011-
dc.identifier.issn0167-8655MIAR
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dc.identifier.urihttp://hdl.handle.net/10609/122026-
dc.description.abstractReal-time mobility data is useful for several applications such as planning transports in metropolitan ar- eas or localizing services in towns. However, if such data is collected without any privacy protection it may reveal sensible locations and pose safety risks to an individual associated to it. Thus, mobility data must be anonymized preferably at the time of collection. In this paper, we consider the SwapMob algo- rithm that mitigates privacy risks by swapping partial trajectories. We formalize the concept of sufficient sanitizer and show that the SwapMob algorithm is a sufficient sanitizer for various statistical decision problems. That is, it preserves the aggregate information of the spatial database in the form of sufficient statistics and also provides privacy to the individuals. This may be used for personalized assistants taking advantage of users' locations, so they can ensure user privacy while providing accurate response to the user requirements. We measure the privacy provided by SwapMob as the Adversary Information Gain, which measures the capability of an adversary to leverage his knowledge of exact data points to infer a larger segment of the sanitized trajectory. We test the utility of the data obtained after applying Swap- Mob sanitization in terms of Origin-Destination matrices, a fundamental tool in transportation modelling.en
dc.language.isoeng-
dc.publisherPattern Recognition Letters-
dc.relation.ispartofPattern Recognition Letters, 2020, 131()-
dc.relation.urihttps://doi.org/10.1016/j.patrec.2020.02.011-
dc.rightsCC BY-
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/es/-
dc.subjectprivacy preserving mobility data miningen
dc.subjectreal-time mobility data anonymizationen
dc.subjecttrajectory anonymizationen
dc.subjectsufficient sanitizeren
dc.subjectintelligent transportation systemsen
dc.subjectorigin-Destination matricesen
dc.subjectprivacidad en la minería de datos de movilidades
dc.subjectprivacitat en la mineria de dades de mobilitatca
dc.subjectanonimització de dades de mobilitat en temps realca
dc.subjectanonimización de datos de movilidad en tiempo reales
dc.subjectanonimización de la trayectoriaes
dc.subjectanonimització de la trajectòriaca
dc.subjectsistemes intel·ligents de transportca
dc.subjectsistemas inteligentes de transportees
dc.subjectmatrices origen-destinoes
dc.subjectmatrius origen-destíca
dc.subjectdepuración suficientees
dc.subjectdepuració suficientca
dc.subject.lcshData miningen
dc.titleSwapping trajectories with a sufficient sanitizer-
dc.typeinfo:eu-repo/semantics/article-
dc.subject.lemacMineria de dadesca
dc.subject.lcshesMinería de datoses
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess-
dc.identifier.doi10.1016/j.patrec.2020.02.011-
dc.gir.idAR/0000007664-
dc.relation.projectIDinfo:eu-repo/grantAgreement/RTI2018-095094-B-C22-
dc.relation.projectIDinfo:eu-repo/grantAgreement/TIN2014-57364-C2-2-R-
dc.type.versioninfo:eu-repo/semantics/publishedVersion-
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