Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-40285-2_31
DC FieldValue
dc.titlePublishing trajectory with differential privacy: A priori vs. a posteriori sampling mechanisms
dc.contributor.authorShao, D.
dc.contributor.authorJiang, K.
dc.contributor.authorKister, T.
dc.contributor.authorBressan, S.
dc.contributor.authorTan, K.-L.
dc.date.accessioned2014-07-04T03:14:49Z
dc.date.available2014-07-04T03:14:49Z
dc.date.issued2013
dc.identifier.citationShao, D.,Jiang, K.,Kister, T.,Bressan, S.,Tan, K.-L. (2013). Publishing trajectory with differential privacy: A priori vs. a posteriori sampling mechanisms. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8055 LNCS (PART 1) : 357-365. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-40285-2_31" target="_blank">https://doi.org/10.1007/978-3-642-40285-2_31</a>
dc.identifier.isbn9783642402845
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/78308
dc.description.abstractIt is now possible to collect and share trajectory data for any ship in the world by various means such as satellite and VHF systems. However, the publication of such data also creates new risks for privacy breach with consequences on the security and liability of the stakeholders. Thus, there is an urgent need to develop methods for preserving the privacy of published trajectory data. In this paper, we propose and comparatively investigate two mechanisms for the publication of the trajectory of individual ships under differential privacy guarantees. Traditionally, privacy and differential privacy is achieved by perturbation of the result or the data according to the sensitivity of the query. Our approach, instead, combines sampling and interpolation. We present and compare two techniques in which we sample and interpolate (a priori) and interpolate and sample (a posteriori), respectively. We show that both techniques achieve a (0, δ) form of differential privacy. We analytically and empirically, with real ship trajectories, study the privacy guarantee and utility of the methods. © 2013 Springer-Verlag.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-40285-2_31
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1007/978-3-642-40285-2_31
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume8055 LNCS
dc.description.issuePART 1
dc.description.page357-365
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

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