<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">A. Oganian</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Masking methods that preserve positivity constraints in microdata</style></title><secondary-title><style face="normal" font="default" size="100%">J. Statist. Planning Inf.</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">constraints</style></keyword><keyword><style  face="normal" font="default" size="100%">Positivity</style></keyword><keyword><style  face="normal" font="default" size="100%">SDL method</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical disclosure limitation (SDL)</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">141</style></volume><pages><style face="normal" font="default" size="100%">31-41</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Statistical agencies have conflicting obligations to protect confidential information provided by respondents to surveys or censuses and to make data available for research and planning activities. When the microdata themselves are to be released, in order to achieve these conflicting objectives, statistical agencies apply statistical disclosure limitation (SDL) methods to the data, such as noise addition, swapping or microaggregation. Some of these methods do not preserve important structure and constraints in the data, such as positivity of some attributes or inequality constraints between attributes. Failure to preserve constraints is not only problematic in terms of data utility, but also may increase disclosure risk. In this paper, we describe a method for SDL that preserves both positivity of attributes and the mean vector and covariance matrix of the original data. The basis of the method is to apply multiplicative noise with the proper, data-dependent covariance structure.&lt;/p&gt;
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