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Computational improvements in parallelized k-anonymous microaggregation of large databases
dc.contributor.author | Mezher, Ahmad Mohamad |
dc.contributor.author | Garcia Alvarez, Alejandro |
dc.contributor.author | Rebollo Monedero, David |
dc.contributor.author | Forné Muñoz, Jorge |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Telemàtica |
dc.date.accessioned | 2017-12-20T16:27:20Z |
dc.date.issued | 2017 |
dc.identifier.citation | Mezher, A., Garcia, A., Rebollo-Monedero, D., Forne, J. Computational improvements in parallelized k-anonymous microaggregation of large databases. A: IEEE International Conference on Distributed Computing Systems. "Distributed Computing Systems Workshops (ICDCSW), 2017 IEEE 37th International Conference on". Atlanta: 2017, p. 258-264. |
dc.identifier.uri | http://hdl.handle.net/2117/112337 |
dc.description.abstract | The technical contents of this paper fall within the field of statistical disclosure control (SDC), which concerns the postprocessing of the demographic portion of the statistical results of surveys containing sensitive personal information, in order to effectively safeguard the anonymity of the participating respondents. The concrete purpose of this study is to improve the efficiency of a widely used algorithm for k-anonymous microaggregation, known as maximum distance to average vector (MDAV), to vastly accelerate its execution without affecting its excellent functional performance with respect to competing methods. The improvements put forth in this paper encompass algebraic modifications and the use of the basic linear algebra subprograms (BLAS) library, for the efficient parallel computation of MDAV on CPU. |
dc.format.extent | 7 p. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
dc.subject.lcsh | Database management |
dc.subject.other | parallelized k-anonymous microaggregation |
dc.subject.other | large databases |
dc.subject.other | statistical disclosure control |
dc.subject.other | sensitive personal information |
dc.subject.other | maximum distance to average vector |
dc.subject.other | MDAV |
dc.subject.other | algebraic modifications |
dc.subject.other | linear algebra subprograms |
dc.subject.other | BLAS library |
dc.subject.other | CPU |
dc.subject.other | parallel computation |
dc.title | Computational improvements in parallelized k-anonymous microaggregation of large databases |
dc.type | Conference report |
dc.subject.lemac | Bases de dades -- Gestió |
dc.contributor.group | Universitat Politècnica de Catalunya. ISG - Grup de Seguretat de la Informació |
dc.identifier.doi | 10.1109/ICDCSW.2017.43 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://ieeexplore.ieee.org/document/7979826/keywords |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 21160664 |
dc.description.version | Postprint (published version) |
dc.date.lift | 10000-01-01 |
local.citation.author | Mezher, A.; Garcia, A.; Rebollo-Monedero, D.; Forne, J. |
local.citation.contributor | IEEE International Conference on Distributed Computing Systems |
local.citation.pubplace | Atlanta |
local.citation.publicationName | Distributed Computing Systems Workshops (ICDCSW), 2017 IEEE 37th International Conference on |
local.citation.startingPage | 258 |
local.citation.endingPage | 264 |