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dc.contributor.authorCastro Pérez, Jordi
dc.contributor.authorGonzález Alastrué, José Antonio
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.date.accessioned2018-04-16T08:06:37Z
dc.date.available2018-04-16T08:06:37Z
dc.date.issued2017-05-23
dc.identifier.citationCastro, J., Gonzalez, J. "A linear optimization based method for data privacy in statistical tabular data". 2017.
dc.identifier.urihttp://hdl.handle.net/2117/116308
dc.description.abstractNational Statistical Agencies routinely disseminate large amounts of data. Prior to dissemination these data have to be protected to avoid releasing confidential information. Controlled tabular adjustment (CTA) is one of the available methods for this purpose. CTA formulates an optimization problem that looks for the safe table which is closest to the original one. The standard CTA approach results in a mixed integer linear optimization (MILO) problem, which is very challenging for current technology. In this work we present a much less costly variant of CTA that formulates a multiobjective linear optimization (LO) problem, where binary variables are pre-fixed, and the resulting continuous problem is solved by lexicographic optimization. Extensive computational results are reported using both commercial (CPLEX and XPRESS) and open source (Clp) solvers, with either simplex or interior-point methods, on a set of real instances. Most instances were successfully solved with the LO-CTA variant in less than one hour, while many of them are computationally very expensive with the MILO-CTA formulation. The interior-point method outperformed simplex in this particular application.
dc.format.extent27 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa
dc.subject.otherlinear optimization
dc.subject.otherinterior-point methods
dc.subject.otherbenchmarking
dc.subject.otherlexicographic optimization
dc.subject.otherdata science
dc.subject.otherdata privacy
dc.subject.otherstatistical disclosure control
dc.titleA linear optimization based method for data privacy in statistical tabular data
dc.typeExternal research report
dc.contributor.groupUniversitat Politècnica de Catalunya. GNOM - Grup d'Optimització Numèrica i Modelització
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::90 Operations research, mathematical programming
dc.relation.publisherversionhttp://www-eio.upc.edu/~jcastro/publications/reports/dr2017-02.pdf
dc.rights.accessOpen Access
drac.iddocument22318426
dc.description.versionPreprint
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/261565-INFRASTRUCTURES-2010-2
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/PE2016-2018/MTM2015-65362-R
upcommons.citation.authorCastro, J., Gonzalez, J.
upcommons.citation.publishedtrue


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