Multiplicative noise for masking numerical microdata with constraints
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 prov...
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| Tipo de documento: | artigo |
| Data de publicação: | 2011 |
| País: | España |
| Recursos: | Universitat Autònoma de Barcelona |
| Repositório: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglês |
| OAI Identifier: | oai:ddd.uab.cat:97850 |
| Acesso em linha: | https://ddd.uab.cat/record/97850 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Statistical disclosure limitation (SDL) SDL method Multiplicative noise Positivity and inequality constraints |
| Resumo: | 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. |
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