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M. Templ:
"Providing data with high utility and no disclosure risk for the public and researchers: an evaluation by advanced statistical disclosure risk methods";
Vortrag: CDAM 2013 10th International Conference Computer Data Analysis & Modeling 2013 Theoretical & Applied Stochastics, Minsk, Weißrussland, Belarus (eingeladen); 13.09.2013 - 14.09.2013.

The demand of data from surveys, registers or other data sets containing
sensible information on people or enterprises have been increased signi cantly
over the last years. However, before providing data to the public or to researchers,
con dentiality has to be respected for any data set containing sensible individual
information. Con dentiality 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 summa-
rized. These methods are nally 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.

Statistical Disclosure Control, Data Utility, Disclosure Risk

http://www.cdam.bsu.by/en/main.aspx?guid=2781