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Computational improvements in parallelized k-anonymous microaggregation of large databases

Mezher, A.; Garcia, A.; Rebollo-Monedero, D.; Forne, J.
Tipus d'activitat
Presentació treball a congrés
Nom de l'edició
2017 IEEE 37th International Conference on Distributed Computing Systems Workshops
Any de l'edició
Data de presentació
Llibre d'actes
Distributed Computing Systems Workshops (ICDCSW), 2017 IEEE 37th International Conference on
Pàgina inicial
Pàgina final
DOI Obrir en finestra nova
Projecte finançador
INcident monitoRing In Smart COmmunities (INRISCO). QoS and Privacy. National Spanish project TEC2014-54335-C4-1-R
Repositori Obrir en finestra nova
URL Obrir en finestra nova
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),...
Paraules clau
Parallelized K-anonymous Microaggregation, Large Databases, Statistical Disclosure Control, Sensitive Personal Information, Maximum Distance To Average Vector, Mdav, Algebraic Modifications, Linear Algebra Subprograms, Blas Library, Cpu, Parallel Computation
Grup de recerca
ISG - Grup de Seguretat de la Informació