Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysis
Por:
Monedero, DR, Mezher, AM, Colome, XC, Forne, J, Soriano, M
Publicada:
1 ene 2019
Resumen:
k-Anonymous microaggregation is a widespread technique to address the problem of protecting the privacy of the respondents involved beyond the mere suppression of their identifiers, in applications where preserving the utility of the information disclosed is critical. Unfortunately, microaggregation methods with high data utility may impose stringent computational demands when dealing with datasets containing a large number of records and attributes. This work proposes and analyzes various anonymization methods which draw upon the algebraic-statistical technique of principal component analysis (PCA), in order to effective reduce the number of attributes processed, that is, the dimension of the multivariate microaggregation problem at hand. By preserving to a high degree the energy of the numerical dataset and carefully choosing the number of dominant components to process, we manage to achieve remarkable reductions in running time and memory usage with negligible impact in information utility. Our methods are readily applicable to high-utility SDC of large-scale datasets with numerical demographic attributes. © 2019 The Authors. Preprint submitted to Elsevier, Inc. © 2019 Elsevier Inc.
Filiaciones:
Monedero, DR:
Univ Politecn Cataluna, Dept Telemat Engn, E-08034 Barcelona, Spain
Mezher, AM:
Univ Politecn Cataluna, Dept Telemat Engn, E-08034 Barcelona, Spain
Colome, XC:
Univ Politecn Cataluna, Dept Telemat Engn, E-08034 Barcelona, Spain
Forne, J:
Univ Politecn Cataluna, Dept Telemat Engn, E-08034 Barcelona, Spain
Soriano, M:
Univ Politecn Cataluna, Dept Telemat Engn, E-08034 Barcelona, Spain
CTTC, E-08860 Barcelona, Spain
Green Published, All Open Access; Green Open Access
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