Statistical Approaches To Protecting Confidentiality For Microdata And Their Effects On The Quality Of Statistical Inferences

When sharing microdata (i.e., data on individuals) with the public, organizations face competing objectives. On the one hand, they strive to release data files that are useful for a wide range of statistical purposes and easy for secondary data users to analyze with standard statistical methods. On the other hand, they must protect the confidentiality of data subjects’ identities and sensitive attributes from attacks by ill-intentioned data users. To address such threats, organizations typically alter microdata before sharing it with others; in fact, most public-use data sets with unrestricted access have undergone one or more disclosure protection treatments. This research synthesis reviews statistical approaches to protecting data confidentiality commonly used by government agencies and survey organizations, with particular emphasis on their impacts on the accuracy of secondary data analyses. In general terms, it discusses potential biases that can result from the disclosure treatments, as well as when and how it is possible to avoid or correct those biases. The synthesis is intended for social scientists doing secondary data analysis with microdata; it does not prescribe best practices for implementing disclosure protection methods or gauging disclosure risks when disseminating microdata.