<?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%">L. H. Cox</style></author><author><style face="normal" font="default" size="100%">S. K. Kinney</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Risk-utility paradigms for statistical disclosure limitation: How to think, but not how to act (with discussion)</style></title><secondary-title><style face="normal" font="default" size="100%">International Statistical Review</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">79</style></volume><pages><style face="normal" font="default" size="100%">160-199</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Risk-utility formulations for problems of statistical disclosure limitation are now common. We argue that these approaches are powerful guides to official statistics agencies in regard to how to think about disclosure limitation problems, but that they fall short in essential ways from providing a sound basis for acting upon the problems. We illustrate this position in three specific contexts—transparency, tabular data and survey weights, with shorter consideration of two key emerging issues—longitudinal data and the use of administrative data to augment surveys.&lt;/p&gt;
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