Autor según el artículo: Domingo-Ferrer, J. Sánchez, D. Rufian-Torrell, G.

Departamento: Enginyeria Informàtica i Matemàtiques

Resumen: Nominal attributes are very common in data sets about individuals, specifically medical data like patient healthcare records. Attributes of this type tend to be sensitive due to their personal nature. If public-use data sets need to be released, e.g. for clinical research purposes, data should be first anonymized. However, since most anonymization methods omit data semantics when dealing with nominal attributes (e.g. in a medical data set diagnosis is a nominal attribute), anonymization results in unnecessary information loss for such attributes, which is especially serious given their analytical importance. In this paper, we present a knowledge-based numerical mapping for nominal attributes that captures and quantifies their underlying semantics. Using this mapping, we show how to compute semantically and mathematically coherent mean, variance and covariance functions for nominal attributes; we also propose a distance measure between records containing numerical and nominal attributes. Thus, the proposed mapping allows adapting to nominal data some statistical disclosure control anonymization methods originally designed for numerical attributes. Evaluation results obtained for one of these methods applied to real patient discharge data shows that the use of our mapping retains better the semantics of original data and, hence, it yields anonymized data with better utility for clinical research.

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 0020-0255

Página final: 48

Volumen de revista: 242

Versión del articulo depositado: info:eu-repo/semantics/acceptedVersion

Enlace a la fuente original: http://www.sciencedirect.com/science/article/pii/S0020025513003186

DOI del artículo: 10.1016/j.ins.2013.04.021

Entidad: Universitat Rovira i Virgili.

Año de publicación de la revista: 2013

Página inicial: 35