Providing Data with High Utility and No Disclosure Risk for the Public and Researchers: An Evaluation by Advanced Statistical Disclosure Risk Methods

Abstract

The demand of data from surveys, registers or other data sets containing sensible information on people or enterprises have been increased significantly over the last years. However, before providing data to the public or to researchers, confidentiality has to be respected for any data set containing sensible individual information. Confidentiality can be achieved by applying statistical disclosure control (SDC) methods to the data. The research on SDC methods becomes more and more important in the last years because of an increase of the awareness on data privacy and because of the fact that more and more data are provided to the public or to researchers. However, for legal reasons this is only visible when the released data has (very) low disclosure risk. In this contribution existing disclosure risk methods are review and summarized. These methods are finally applied on a popular real-world data set - the Structural Earnings Survey (SES) of Austria. It is shown that the application of few selected anonymisation methods leads to well-protected anonymised data with high data utility and low information loss.