Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysis
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...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) |
| Repositorio: | r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) |
| OAI Identifier: | oai:cttc.fundanetsuite.com:p1434 |
| Acceso en línea: | https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1434 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068614484&doi=10.1016%2fj.ins.2019.07.042&partnerID=40&md5=6e5191eb8ab29c7516fbf387f649f99d |
| Access Level: | acceso abierto |
| Palabra clave: | Algebra Data privacy Large dataset Numerical methods Computational demands Data utilities Information utility K-Anonymity Large-scale datasets Microaggregation Statistical disclosure Control Statistical techniques Principal component analysis |
| Sumario: | 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. |
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