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A linear optimization based method for data privacy in statistical tabular data
dc.contributor.author | Castro Pérez, Jordi |
dc.contributor.author | González Alastrué, José Antonio |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa |
dc.date.accessioned | 2018-04-16T08:06:37Z |
dc.date.available | 2018-04-16T08:06:37Z |
dc.date.issued | 2017-05-23 |
dc.identifier.citation | Castro, J., Gonzalez, J. "A linear optimization based method for data privacy in statistical tabular data". 2017. |
dc.identifier.uri | http://hdl.handle.net/2117/116308 |
dc.description.abstract | National 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.extent | 27 p. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa |
dc.subject.other | linear optimization |
dc.subject.other | interior-point methods |
dc.subject.other | benchmarking |
dc.subject.other | lexicographic optimization |
dc.subject.other | data science |
dc.subject.other | data privacy |
dc.subject.other | statistical disclosure control |
dc.title | A linear optimization based method for data privacy in statistical tabular data |
dc.type | External research report |
dc.contributor.group | Universitat Politècnica de Catalunya. GNOM - Grup d'Optimització Numèrica i Modelització |
dc.description.peerreviewed | Peer Reviewed |
dc.subject.ams | Classificació AMS::90 Operations research, mathematical programming |
dc.relation.publisherversion | http://www-eio.upc.edu/~jcastro/publications/reports/dr2017-02.pdf |
dc.rights.access | Open Access |
local.identifier.drac | 22318426 |
dc.description.version | Preprint |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/FP7/261565/EU/Virtual multidisciplinary EnviroNments USing Cloud infrastructures/VENUS-C |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//MTM2015-65362-R/ES/OPTIMIZACION DE MUY GRAN ESCALA: METODOS Y APLICACIONES/ |
local.citation.author | Castro, J.; Gonzalez, J. |
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