Providing Data With High Utility And No Disclosure Risk For The Public and Researchers: An Evaluation By Advanced Statistical Disclosure Risk Methods
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
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