97850 articleid 16962281vSpecialp99 eng Oganian, Anna Georgia Southern University 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 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. Tots els drets reservats http://www.europeana.eu/rights/rr-f/ Anglès Article de fons Statistical disclosure limitation (SDL) SDL method Multiplicative noise Positivity and inequality constraints info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Vol. Special, Núm. (2011), p. 99-112 SORT : statistics and operations research transactions 1696-2281 14 308278 http://ddd.uab.cat/uab/sort/sort_a2011vSPECIAL/sort_a2011nSPECIALp99.pdf 0099 112 Special sort_a2011vSPECIAL 2011 ARTPUB SORT UAB artpubuab oai:ddd.uab.cat:97850 driver