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Computational improvements in parallelized k-anonymous microaggregation of large databases

Autor
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
2017
Data de presentació
2017-06-05
Llibre d'actes
Distributed Computing Systems Workshops (ICDCSW), 2017 IEEE 37th International Conference on
Pàgina inicial
258
Pàgina final
264
DOI
https://doi.org/10.1109/ICDCSW.2017.43 Obrir en finestra nova
Projecte finançador
INcident monitoRing In Smart COmmunities (INRISCO). QoS and Privacy. National Spanish project TEC2014-54335-C4-1-R
Repositori
http://hdl.handle.net/2117/112337 Obrir en finestra nova
URL
http://ieeexplore.ieee.org/document/7979826/keywords Obrir en finestra nova
Resum
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),...
Citació
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.
Paraules clau
BLAS library, CPU, MDAV, algebraic modifications, large databases, linear algebra subprograms, maximum distance to average vector, parallel computation, parallelized k-anonymous microaggregation, sensitive personal information, statistical disclosure control
Grup de recerca
SISCOM - Smart Services for Information Systems and Communication Networks

Participants