t_ex

A dataset containing the categories observations belongs to.
Each observation is included in two categories : one from
the nomenclature z1 and one from the nomenclature z2.

datasets

An algorithm based on graph theory tools to detect differentiation
problems. A differentiation problem occurs when aggregated
data are disseminated according to two different nomenclatures. By making the
difference for an additive variable X between an aggregate composed of categories
of the first nomenclature and an other aggregate, included in that first
aggregate, composed of categories of the second nomenclature,
it is sometimes possible to derive X on a small aggregate of records
which could then lead to a break of confidentiality.
The purpose of this package is to detect the set of aggregates composed of
categories of the first nomenclature which lead to a differentiation problem,
when given a confidentiality threshold. Reference: Vianney Costemalle (2019) <doi: 10.3233/SJI-190564>.

Vianney Costemalle

diffman

Detect Differentiation Problems

Arlindo Dos Santos

Francois Semecurbe

t_ex function

A data frame with 34695 rows and 3 variables:<dl class="dl-horizontal">
 <dt>id</dt><dd>number to identify the observation</dd>
 <dt>z1</dt><dd>first nomenclature. Each category is coded by an integer (between 5 and 363)</dd>
 <dt>z2</dt><dd>second nomenclature. Each category is coded by an integer (between 1 and 406)</dd>
</dl>

Format

A simulated dataset of 34695 observations — t_ex

A data frame with 34695 rows and 3 variables:<dl class='dl-horizontal'>
 <dt>id</dt><dd>number to identify the observation</dd>
 <dt>z1</dt><dd>first nomenclature. Each category is coded by an integer (between 5 and 363)</dd>
 <dt>z2</dt><dd>second nomenclature. Each category is coded by an integer (between 1 and 406)</dd>
</dl>

t_ex: A simulated dataset of 34695 observations

Description

Usage

Arguments

Format