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Multiplicative noise for masking numerical microdata with constraints
Oganian, Anna (Georgia Southern University)

Fecha: 2011
Resumen: Before releasing databases which contain sensitive information about individuals, statistical agencies have to apply Statistical Disclosure Limitation (SDL) methods to such data. The goal of these methods is to minimize the risk of disclosure of the confidential information and at the same time provide legitimate data users with accurate information about the population of interest. SDL methods applicable to the microdata (i. e. collection of individual records) are often called masking methods. In this paper, several multiplicative noise masking schemes are presented. These schemes are designed to preserve positivity and inequality constraints in the data together with the vector of means and covariance matrix.
Derechos: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Lengua: Anglès
Documento: article ; recerca ; publishedVersion
Materia: Statistical disclosure limitation (SDL) ; SDL method ; Multiplicative noise ; Positivity and inequality constraints
Publicado en: SORT : statistics and operations research transactions, Vol. Special, Núm. (2011) , p. 99-112, ISSN 1696-2281

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