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 sensibleinformation 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 berespected for any data set containing sensible individual information. Confidentiality canbe achieved by applying statistical disclosure control (SDC) methods to the data. Theresearch on SDC methods becomes more and more important in the last years because ofan increase of the awareness on data privacy and because of the fact that more and moredata are provided to the public or to researchers. However, for legal reasons this is onlyvisible when the released data has (very) low disclosure risk.In this contribution existing disclosure risk methods are review and summarized. Thesemethods are finally applied on a popular real-world data set - the Structural EarningsSurvey (SES) of Austria. It is shown that the application of few selected anonymisationmethods leads to well-protected anonymised data with high data utility and low informationloss.