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This paper provides an overview of methods of masking microdata so that the data can be placed in public-use files. It divides the methods according to whether they have been demonstrated to provide analytic properties or not. For those methods that have been shown to provide one or two sets of analytic properties in the masked data, we indicate where the data may have limitations for most analyses and how re-identification might or can be performed. We cover several methods for producing synthetic data and possible computational extensions for better automating the creation of the underlying statistical models. We finish by providing background on analysis-specific and general information-loss metrics to stimulate research.