ai_utility_score

Computes a weighted score from suppression rate (NAs from localSuppression),
category loss (levels merged by groupAndRename), and numerical information
loss (IL1). Lower is better; 0 means no information loss.

internal

Data from statistical agencies and other institutions are mostly
confidential. This package, introduced in Templ, Kowarik and Meindl (2017) <doi:10.18637/jss.v067.i04>, can be used for the generation of anonymized
(micro)data, i.e. for the creation of public- and scientific-use files.
The theoretical basis for the methods implemented can be found in Templ (2017) <doi:10.1007/978-3-319-50272-4>.
Various risk estimation and anonymization methods are included. Note that the package
includes a graphical user interface published in Meindl and Templ (2019) <doi:10.3390/a12090191> that allows to use various methods of this
package.

Matthias Templ

sdcMicro

Statistical Disclosure Control Methods for Anonymization of Data
and Risk Estimation

Bernhard Meindl

Alexander Kowarik

Johannes Gussenbauer

Organisation For Economic Co-Operation And Development 

Statistics Netherlands 

Pascal Heus 

ai_utility_score function

<dl><dt>sdcObj_orig</dt>
<dd>Original sdcMicroObj (before anonymization).</dd>
<dt>sdcObj_anon</dt>
<dd>Anonymized sdcMicroObj (after anonymization).</dd>
<dt>weights</dt>
<dd>Numeric vector of length 3: weights for suppression rate,
category loss, and IL1. Default <code>c(1/3, 1/3, 1/3)</code>.</dd></dl>

Arguments

Combined utility score for anonymization quality — ai_utility_score

<dl>

<dt>sdcObj_orig</dt>
<dd>Original sdcMicroObj (before anonymization).</dd>


<dt>sdcObj_anon</dt>
<dd>Anonymized sdcMicroObj (after anonymization).</dd>


<dt>weights</dt>
<dd>Numeric vector of length 3: weights for suppression rate,
category loss, and IL1. Default <code>c(1/3, 1/3, 1/3)</code>.</dd>

</dl>

ai_utility_score: Combined utility score for anonymization quality

Description

Usage

Value

Arguments