Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6284
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dc.contributor.authorAbul, Osman-
dc.contributor.authorBonchi, Francesco-
dc.contributor.authorNanni, Mirco-
dc.date.accessioned2021-09-11T15:35:37Z-
dc.date.available2021-09-11T15:35:37Z-
dc.date.issued2010en_US
dc.identifier.issn0306-4379-
dc.identifier.issn1873-6076-
dc.identifier.urihttps://doi.org/10.1016/j.is.2010.05.003-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6284-
dc.description.abstractPreserving individual privacy when publishing data is a problem that is receiving increasing attention. Thanks to its simplicity the concept of k-anonymity, introduced by Samarati and Sweeney [1], established itself as one fundamental principle for privacy preserving data publishing. According to the k-anonymity principle, each release of data must be such that each individual is indistinguishable from at least k-1 other individuals. In this article we tackle the problem of anonymization of moving objects databases. We propose a novel concept of k-anonymity based on co-localization, that exploits the inherent uncertainty of the moving object's whereabouts. Due to sampling and imprecision of the positioning systems (e.g., GPS), the trajectory of a moving object is no longer a polyline in a three-dimensional space, instead it is a cylindrical volume, where its radius delta represents the possible location imprecision: we know that the trajectory of the moving object is within this cylinder, but we do not know exactly where. If another object moves within the same cylinder they are indistinguishable from each other. This leads to the definition of (k,delta)-anonymity for moving objects databases. We first characterize the (k,delta)-anonymity problem, then we recall NWA (Never Walk Alone), a method that we introduced in [2] based on clustering and spatial perturbation. Starting from a discussion on the limits of NWA we develop a novel clustering method that, being based on EDR distance [3], has the important feature of being time-tolerant. As a consequence it perturbs trajectories both in space and time. The novel method, named W4M (Wait for Me), is empirically shown to produce higher quality anonymization than NWA, at the price of higher computational requirements. Therefore, in order to make W4M scalable to large datasets, we introduce two variants based on a novel (and computationally cheaper) time-tolerant distance function, and on chunking. All the variants of W4M(1) are empirically evaluated in terms of data quality and efficiency, and thoroughly compared to their predecessor NWA.(2) Data quality is assessed both by means of objective measures of information distortion, and by more usability oriented measure, i.e., by comparing the results of (i) spatio-temporal range queries and (ii) frequent pattern mining, executed on the original database and on the (k,delta)-anonymized one. Experimental results over both real-world and synthetic mobility data confirm that, for a wide range of values of delta and k, the relative distortion introduced by our anonymization methods is kept low. Moreover, the techniques introduced to make W4M scalable to large datasets, achieve their goal without giving up data quality in the anonymization process. (C) 2010 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipOsman Abul ERCIM; EUEuropean Commission [IST-6FP-014915]; TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [108E016]en_US
dc.description.sponsorshipThis work was started during the tenure of Osman Abul ERCIM fellowship at ISTI-CNR. Mirco Nanni is supported by the EU project GeoPKDD (IST-6FP-014915). Osman Abul is supported by TUBITAK, project number 108E016.en_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofInformation Systemsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMoving objects databasesen_US
dc.subjectTrajectoriesen_US
dc.subjectAnonymityen_US
dc.subjectUncertaintyen_US
dc.subjectClusteringen_US
dc.titleAnonymization of Moving Objects Databases by Clustering and Perturbationen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume35en_US
dc.identifier.issue8en_US
dc.identifier.startpage884en_US
dc.identifier.endpage910en_US
dc.identifier.wosWOS:000281341200003en_US
dc.identifier.scopus2-s2.0-77955557409en_US
dc.institutionauthorAbul, Osman-
dc.identifier.doi10.1016/j.is.2010.05.003-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.3. Department of Computer Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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