<abstract xmlns="http://www.w3.org/1999/xhtml"><p> In this article we propose a simple and versatile method for limiting disclosure in continuous microdata based on Principal Component Analysis (PCA). Instead of perturbing the original variables, we propose to alter the principal components, as they contain the same information but are uncorrelated, which permits working on each component separately, reducing processing times. The number and weight of the perturbed components determine the level of protection and distortion of the masked data. The method provides preservation of the mean vector and the variance-covariance matrix. Furthermore, depending on the technique chosen to perturb the principal components, the proposed method can provide masked, hybrid or fully synthetic data sets. Some examples of application and comparison with other methods previously proposed in the literature (in terms of disclosure risk and data utility) are also included.</p></abstract>

In this article we propose a simple and versatile method for limiting disclosure in continuous microdata based on Principal Component Analysis (PCA). Instead of perturbing the original variables, we propose to alter the principal components, as they contain the same information but are uncorrelated, which permits working on each component separately, reducing processing times. The number and weight of the perturbed components determine the level of protection and distortion of the masked data. The method provides preservation of the mean vector and the variance-covariance matrix. Furthermore, depending on the technique chosen to perturb the principal components, the proposed method can provide masked, hybrid or fully synthetic data sets. Some examples of application and comparison with other methods previously proposed in the literature (in terms of disclosure risk and data utility) are also included.

A Simple Method for Limiting Disclosure in Continuous Microdata Based on Principal Component Analysis

Department of Computer Science and Mathematics, Universitat Rovira i Virgili, 43007 Tarragona, Spain

Department of Statistics and Operations Research III, Complutense University of Madrid, 28040 Madrid,

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

In this article we propose a simple and versatile method for limiting disclosure in continuous microdata based on Principal Component Analysis (PCA). Instead...

A Simple Method for Limiting Disclosure in Continuous Microdata Based on Principal Component Analysis

Online veröffentlicht: 21. Feb. 2017

Seitenbereich: 15 - 41

Eingereicht: 01. Sept. 2015

Akzeptiert: 01. Aug. 2016

DOI: https://doi.org/10.1515/jos-2017-0002

Schlüsselwörter
Statistical disclosure control, microdata protection, hybrid microdata, masking method, propensity score

© by Aida Calviño

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

A Simple Method for Limiting Disclosure in Continuous Microdata Based on Principal Component Analysis

Online veröffentlicht: 21. Feb. 2017

Seitenbereich: 15 - 41

Eingereicht: 01. Sept. 2015

Akzeptiert: 01. Aug. 2016

DOI: https://doi.org/10.1515/jos-2017-0002

SchlüsselwörterStatistical disclosure control, microdata protection, hybrid microdata, masking method, propensity score

© by Aida Calviño

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Statistical disclosure control, microdata protection, hybrid microdata, masking method, propensity score