Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysis

•The primary goal of this work is to reduce the running time of k-anonymous microaggregation algo-rithms operating on datasets with a large quantity of numerical demographic attributes, acting as quasi-identifiers. Principal component analysis (PCA), an algebraic-statistical procedure that construct...

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Bibliographic Details
Published in:	Information sciences Vol. 503; pp. 417 - 443
Main Authors:	Monedero, David Rebollo, Mezher, Ahmad Mohamad, Colomé, Xavier Casanova, Forné, Jordi, Soriano, Miguel
Format:	Journal Article Publication
Language:	English
Published:	Elsevier Inc 01-11-2019
Subjects:	Bases de dades Big data Ciències de la informació Dades massives Data privacy Information science Informàtica K-anonymity Large-scale datasets Microaggregation Principal component analysis Sistemes d'informació Statistical disclosure control Àrees temàtiques de la UPC Data privacy Statistical disclosure control Large-scale datasets Microaggregation k-anonymity Principal component analysis
Online Access:	Get full text
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