Disclosure Risk from Factor Scores

Abstract

Remote access can be a powerful tool for providing data access for external researchers. Since the microdata never leave the secure environment of the data-providing agency, alterations of the microdata can be kept to a minimum. Nevertheless, remote access is not free from risk. Many statistical analyses that do not seem to provide disclosive information at first sight can be used by sophisticated intruders to reveal sensitive information. For this reason the list of allowed queries is usually restricted in a remote setting. However, it is not always easy to identify problematic queries. We therefore strongly support the argument that has been made by other authors: that all queries should be monitored carefully and that any microlevel information should always be withheld. As an illustrative example, we use factor score analysis, for which the output of interest -the factor loading of the variables -seems to be unproblematic. However, as we show in the article, the individual factor scores that are usually returned as part of the output can be used to reveal sensitive information. Our empirical evaluations based on a German establishment survey emphasize that this risk is far from a purely theoretical problem.

Data Smearing: An Approach to Disclosure Limitation for Tabular Data

“…To accomplish this, a disclosure limitation method (DLM) is chosen to protect the sensitive data while allowing the provided data set to retain as much of the utility of the original data as possible. Because quantifying the level of protection and the utility a given DLM provides is difficult (Lambert 1993), comparing DLMs (and thus choosing a method) is not straightforward. Indeed, the level of protection offered by a DLM usually depends on characteristics of the data being published and is usually only quantified with certain restrictions on how the data can be accessed (see, for example Wasserman and Zhou 2010).…”