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Package sdcMicro contains a shiny app that
should help users that are non-experts in R (command-line) to apply
disclosure limitation techniques. For this reason, users may upload
(micro)data files from different software products into the app and then
start to anonymize the dataset by working within the interactive,
graphical user interface (GUI). This document will give an overview of
the functionalities of the graphical user interface which can be started
with sdcApp(). The main functionality of the GUI is:
sdcMicroObj-instancessdcMicroObj-problem instancesdcMicroWe now describe the features of the interactive graphical user
interface in detail. The GUI is separated into 7 main categories, which
can be selected from the navigation bar at the top of the screen.
Initially, some of these pages will be empty and their content changes
once microdata have been uploaded or an sdcMicroObj has
been generated.
We also want to note that throughout the GUI, questionmark signs are shown. Hovering the mouse over these small icons triggers a pop up window with additional information which will help during the anonymization process.
This is the first page that is shown once the graphical user
interface has been started using sdcApp() after loading
package sdcMicro. On this page, the user is presented the
information on how to open this package vignette which contains
extensive information on how to use the GUI. Furthermore, in section
Getting started, users are advised to either upload microdata or to upload a
previously saved problem instance. Clicking on the relevant buttons
brings the user automatically to the page from which the desired
functionality is available.
In section Settings, it is possible to change the
default output path. This path is used whenever the user decides to
export data from the GUI to the hard disk. The default value is the
directory from which the GUI was started (e.g the current working
directory). Once a valid path is entered in the text input field, a
button labelled Update the current output path appears
below the input. Pressing this input updates the path. If successful,
the current path is shown both as placeholder in the text input as well
as in the text above the input field. We note that you can change the
path at any time during the anonymization process. Writing files to disk
will always use the current path.
From this page, the user can also stop the interface by clicking on a
button labelled Stop the GUI. If this feature is used, the
current [uploaded microdata] after modifications as well as the current problem instance (if it has already
been specified) are (invisibly) returned to R. So in case
the interface was started with x <- sdcApp(),
x then contains a list with two elements named
inputdata and sdcObj. This allows one to
continue working in R. Finally, users are shown ways on how
to contact us for bug reports or any other issues.
We now continue to describe the functionality of the user interface in detail.
On this page, the user can either upload data sets stored as files on
the hard drive into the GUI or to select data frames that exist in the
users’ workspace before working the graphical user interface was
started. This allows to perform common data manipulation steps directly
in R before continuing to anonymize the dataset using the
GUI.
We note that the content of this page changes depending on whether microdata have already been uploaded or not. In the former case, the user can view, modify or reset variables from the uploaded dataset as described in chapter Modify microdata. In the latter case, the user is asked to upload data in the GUI. This is described in chapter Upload microdata below.
By default, no microdata are loaded into the GUI. Therefore the user
has to upload some data in the GUI that can later be anonymized. If no
microdata are available, the left-sidebar shows the following options
that can be selected by clicking on the appropriate action button. In
case the selected data could not be used (eg. the data could not be
converted to a data.frame), the user is presented with the
resulting error message and a button Try-again. After
clicking this button, another microdata file can be imported.
This screen allows the user to select data.frames that are available
in the users-workspace when starting the user interface. Two test-data
sets (testdata and testdata2, information on
which is available from ?testdata) that are included in
sdcMicro are always available. Pressing the action button
below the drop-down selection input will make the GUI use the selected
data frame.
Here users can opt to upload a file saved in R binary
format. Users can change the options if character vectors should be
automatically converted to factors and if variables that only contain
missing-values only should be dropped. By clicking on the
Browse button the user needs to select a
rdata-file on disk which he wants to upload. For detailed
explanation on the options, please see the chapter on additional options.
Here users can opt to upload a file exported from SPSS.
Users can change the options if character vectors should be
automatically converted to factors and if variables that contain only
missing-values (‘NA’) only should be dropped. By clicking on the
Browse button the user needs to select a
sav-file on disk which he wants to upload. For detailed
explanation on the options, please see the chapter on additional options.
Here users can opt to upload a file exported from SAS.
Users can change the options if character vectors should be
automatically converted to factors and if variables that contain only
missing-values (‘NA’) only should be dropped. By clicking on the
Browse button the user needs to select a
sas7bdat-file on disk which he wants to upload. For
detailed explanation on the options, please see the chapter on additional options.
Here users can opt to upload a text file where variables are
separated by some characters. Typically these data would be exported
from software such as Excel. It is crucial that users
indicate if the data file has variable names in the first row and how
variables are separated. At this point, users have the option to have
character vectors automatically converted to factor or have variables
that contain only missing-values (‘NA’) dropped when the data are read
into the GUI. For columns read as character (text), the character
" is ignored as quoting character and not imported. By
clicking on the Browse button the user needs to select a
txt or csv-file on disk which he wants to
upload. For detailed explanation on the options, please see the chapter
on additional options.
Here users can opt to upload a file exported from Stata.
Users can change the options if character vectors should be
automatically converted to factors and if variables that contain only
missing-values (‘NA’) only should be dropped. By clicking on the
Browse button the user needs to select a
dta-file on disk which he wants to upload. For detailed
explanation on the options, please see the chapter on additional options.
We now describe the choices users can make when uploading data.
Convert string variables (character vectors) to factor variables?
This option is not available when an existing data frame from the
current workspace is selected/used. For any other selection, this radio
button input has two possible choices, TRUE (the default
value) and FALSE. If TRUE, any variables that
are read into R as character-vectors should be
automatically converted to a factor variable. Each distinct value of the
variable will be a factor level in the imported dataset. If
FALSE, no conversion is applied.
Drop variables with only missing values (NA)?
This option is not available when an existing data frame from the
current workspace is selected/used. For any other selection, this radio
button input has two possible choices, TRUE (the default
value) and FALSE. If TRUE, any variables in
which only NA-values are read in are removed from the data
set. If this option is set to FALSE, these variables (if
any) will not be dropped.
Does the first row contain the variable names?
This option is only available when a text/csv file is imported. This
radio button input has two possible choices, TRUE (the
default value) and FALSE. If TRUE, the first
row of the imported data set will be interpreted as variable names, if
FALSE, variable names are automatically generated.
Select the field separator (Comma, Semicolon, Tab)
This option is only available when a text/csv file is imported. The
radio button input has three possible choices, Comma (the
default value) Semicolon and Tab defining the
value that is used to separate variables in the input file.
Comma: the "," character is used as
separatorSemicolon: the ; character is used as
separatorTab: tabulators (\t) are used as
separatorsSelect File Input:
This option is not available when an existing data frame from the
current workspace should be used. For any other selection, clicking on
the Browse button allows the user to select a file on the
local hard drive. A feature is that only files with the accepted file
ending (e.g .dta when files from Stata
.rdata when data exported from R should be
imported) are shown. This reduces the risk, that a unsuitable file can
be selected. Once a file has been selected, pushing the
Open button immediately uploads the file so that the GUI
can process it. If the file cannot be read into the system successfully,
the user is presented with the resulting error message. If everything
works out smoothly, microdata are now available and the left sidemenu
changes. The user can now start the anonymization process. For further
information, please have a look at the following sections.
Once data have been uploaded, the content of the
Microdata-page changes and users can select from a range of
possibilities on what to do with the current inputdata. Once data are
available, a button Reset inputdata is available at the
bottom of the sidebar. Clicking this button allows to reset or delete
the current input data. However, clicking this button does not
immediately reset the problem. Instead, a pop-up window comes up where
the user has to confirm to reset the current microdata. This action will
be performed, if the user click on the button labelled
Delete current inputdata. If the user clicks
Dismiss, the inputdata remains unchanged.
Below this button, a list of action buttons is shown. Clicking on any of these buttons changes the content of the main column. The currently active selection has a different color than the currently inactive buttons. By default, the first entry (“Display Microdata”) is selected. These entries can be selected by clicking on the desired text or directly on the button. We now continue to describe the features that can be selected.
This is the default selection, after microdata have been successfully imported or uploaded as described in uploading microdata. This page gives a short overview on the microdata. It shows the name of the imported file as well as the number of observations and the number of variables that are available. Below this information, the user is presented with an interactive table containing the current microdata. The variables can be sorted by clicking on the small arrows next to the variable names on top of the table. Also on the top, there is a dropdown field where users can select how many observations should be displayed on one page. On the bottom of the table users can find a dynamic pagination field which allows users to jump to a given “page” of the current table.
On this page users have the possibility to explore variables from the current microdata. Users have to choose a variable by selecting a variable from the dropdown field with label “Choose a variable”. The default value of this input field is the first variable in the dataset. Optionally, a second variable can be selected by choosing a variable from the dropdown field labelled “Choose a second variable (optional)” which has the default value of “None”. Once the variables have been selected, a graph and additional information is presented below. The specific output depends on the number of variable(s) chosen as well as their type:
One variable selected:
factor or
character:In this case, a barplot of the factor levels is shown. Below that, a table showing for each factor level the level itself, how often it occurs and the corresponding percentage is shown. Below that, again the number and percentage of missing values is shown.
integer or
numeric:In this case, a histogram of the selected variable is shown. Below the graph, a table showing main summary statistics (Minimum, Mean, Median, Maximum and 5%-, 25%-, 75%- and 95%-quantiles) are shown. Below this table, the number and percentage of missing values is displayed.
Two variables selected:
integer or
numeric:If both selected variables are continuous (numeric or
integer), a scatterplot of the two variables is displayed.
Below that, the correlation coefficient (Pearson) using only pairwise
complete observations between the two variables is listed. Below that,
two tables are shown. Each table shows the main summary statistics for
one of the selected variables. The information included in the tables
are (as in the case when only one continuous variable is selected) the
Minimum, Mean, Median, Maximum and 5%-, 25%-, 75%- and 95%-quantiles of
the variable. Finally, information on the number and percentage of
missing values is shown for both variables.
factor or
character:In this case, a mosaicplot of the selected variables is shown as well
as a table, containing a cross-tabulation of each levels (or unique
values in case of a character input) that shows the number of
percentages of each combination of codes given the two selected
variables including any combinations with NA. Below this
table, the number and percentage of missing values is displayed for both
selected variables.
factor or
character, the other variable is of type
integer or numeric:In this case, a grouped boxplot of the continous variable (type is
integer or numeric) is shown for each level or
unique value of the non-continuous variable. Below, for each level of
the non-continuous variable, the same summary statistics as already
described above of the continuous variable are shown. Finally, the
number and percentage of missing values is displayed for both selected
variables.
When a microdata set is uploaded, a backup of the unmodified dataset
is saved internally. This allows users to reset any modifications in
variables in the inputdata file which can be done on this page. To do
so, the user needs to select one or more variables from the select field
which is by default empty. When all variables that should be reverted
are selected, one has to click outside the dropdown field to close the
input. Afterwards, an action button labelled
Reset selected variable(s) to their original state occurs
below the input field. Clicking on this button resets the selected
variables to their original state. In case you want to remove already
selected variables, you can just click on these variable names in the
input field and either press the “backspace”- or
“delete”” keys on the keyboard.
In any case, after pressing the action button, the GUI changes to the “Explore variables” page at which the first of the selected variable(s) is shown.
On this page it is possible to decrease the size of the input data.
This is especially useful if one wants to test different parameter
settings and a run on the complete dataset would take long. To reduce
the size of the input dataset, the user has to select a method using the
drop down field
Select a method to restrict the number of records and to
select a value from the slider
Set 'n' for the selected method. The following choices are
possible on how to reduce the dataset, the range of values that can be
chosen from the slider depends on the choice.
n percent of the data: n% of the data will
be randomly chosen. The slider ranges in this case from 1 to 100.the first n observations: the data set is decreased by
only using the first n% records. The slider ranges in this
case from 1 to the total number of records.every n-th observation: This choice allows to create a
simple, systematic sample of input data by selecting every
nth observation. The slider ranges in this case from 1 to
at max 500.exactly n randomly drawn observations: the data set is
decreased by taking a random sample of n% records. The
slider ranges in this case from 1 to the total number of records.When the desired selections have been applied, pushing the button
Create subset performs the actual sub-subsampling. After
the micro data set has been reduced, the user is taken to the Display microdata page where the reduced
dataset can be analyzed.
This page allows to convert numerical variables (numeric
or integer) into factors. Users can choose from a range of
possibilites on how the factor variable should be generated, ranging
from automatic conversion to complete manual control.
By default, two input fields are available. On the left hand side
there is a dropdown select field termed
Choose numeric variables where the user can select from the
list of numeric variables in the input data set. Next to this element,
there is are two radio buttons labelled Use custom breaks?
with two options, no (the default) and yes. If
the no is selected in this input and at least one numeric
variable has been clicked from the select input
Choose numeric variable(s), a button termed
Convert to factor(s) occurs. Pressing this button converts
all of the selected numerical variables into factors. Afterwards, the
user is taken to the explore variables
page where the first of the selected variable(s) is shown.
If, however, the radio buttons Use custom breaks? are
set to yes, the layout of the page changes. In this case,
the user is able to adjust the way the factor variable should be
generated and additional UI elements appear. The first visible change
is, that only one numeric variable can now be selected. Now, a list of
radio buttons labelled Choose a numeric variable is shown
where all available variables are printed below each other. Selecting a
specific variable works by clicking on either the variable name or the
radio button itself. The next choice the user has to make is in a select
input field termed Select algorithm in which
equidistant (the default), logEqui,
equalAmount or manual are possible values.
The remaining user interface is the same for the first three choices
of the Select algorithm input and is slightly different if
manual has been selected. In the first case, a numeric
input field labelled Specify number of intervals is shown
while in the second case a text input field labelled
Specify custom breaks occurs right next to the select input
field where the algorithm can be selected.
If either equidistant, logEqui or
equalAmount are selected, the number specified in the
numeric input field defines the number of levels the new factor will
have. The difference between the methods are:
equidistant: uses breakpoints that generate intervals
of equal length. The number of records in each interval might
differ.logEqui: uses breakpoints that generate intervals of
equal length based on the log transformation of the data. The number of
records in each interval might differ.equalAmount: uses breakpoints such that each
group/interval has the same number of records. The intervals might be of
different length.Selecting manual allows the user to set the breakpoints
manually. Note: make sure that all values are included in the specified
intervals. The syntax in this text field is the way that the breakpoints
(numbers) have to be entered separated by a colon (,). This
sequence of numbers will be interpreted as follows: all values greater
than the value before a colon and all values smaller or equal than the
value after the colon are grouped together. Any non-matched values will
be NA. As an example, entering 1,3,5,9 would
create a factor from a numeric variable by grouping together all values
in x greater than 1 and less or equal to 3 into the first group, all
values greater than 3 and less or equal to 5 into the second group and
all values greater than 5 and less or equal to 9 into a third group. Any
values in x less or equal than 1 or greater or equal to 10 would be
NA. However, -Inf and Inf may be
entered as the first or last value to avoid the generation of
NAs. Already existing missing values in the numeric input
variable will stay NA after the recoding.
What is common for all choices of Select algorithm is
that a button labelled Convert to factor appears if the
information that has been entered is correct. Also, at the bottom of the
page a table with two columns is shown. The first columns shows each of
the unique values of the selected variable while the second column shows
the number of occurances of this value. On pressing the action button,
the selected numeric variable is recoded according the the parameters
that have been chosen. After the recode has been done, the current view
changes to the explore variable page
with the recoded factor variable already being selected.
In this page, variables of type factor and
character from the input data can be converted to numeric
variables. If no such variables are present in the microdata, the user
is shown this information and no conversion is possible. Otherwise, the
user can select one or more variables from the input field labelled
Choose variable(s). Once at least one variable is selected,
a button called Recode to numeric occurs below the variable
selection input. After clicking this button, the conversion is done and
the view changes to the explore
variable page with the first of the recoded variables being
selected.
For variables of type factor, the factor levels are
converted to numeric values. Factor levels that cannot be converted to
numeric values are replaced with missing values (NA). It
should be noted however - as also shown on the GUI - that, for variables
of type character, this feature should be used with care
because internally, the function as.numeric() is used to
perform the conversion. Thus, the resulting numeric vector contains the
underlying numeric (integer) representation of the input vector.
When this option has been selected from the left hand sidebar, it is
possible to modify existing factor variables. The most common use case
is to combine one or more levels of the factor. The other use case is to
rename single factor levels. In order to proceed, the user must first
select an existing factor variable from the select input labelled
Choose factor variable. Below this box, there is another
input termed Select Levels to recode/combine. In this
input, all the levels of the active factor variable can be choosen by
clicking on the are selectable. Already selected levels may be removed
again by clicking on them with the mouse and pressing either the
“backspace”- or “delete” keys on the keyboard.
If at least one factor level is selected, a text input called
New label for recoded values as well as a radio input
labelled Add missing values to new factor level? appear. By
default, the textbox containing the name of the new level is computed by
joining the selected factor levels together using the character
_ as separator. By clicking into this text field, users can
also start to enter a custom name for the new factor level. If only one
level has been selected in Select Levels to recode/combine,
entering a value different than the default value in this input leads to
renaming of this specific factor level. The radio button input labelled
Add missing values to new factor level? is set to
no by default. If it is changed to yes, any missing
(NA) values in the factor are added to the new level.
Below these input fields a button labelled
Group factor levels and a barplot showing the absolute
number of the current levels of the factors are shown. Pressing the
action button results in updating the factor. In this case, the page
refreshes and the plot adjusts the the changes that have been
applied.
On this page, the user is able to generate a new variable based on
two ore more variables from the current micro data. The reason for this
is that several anonymization techiques that are explained here and here can by applied independently to
subgroups of the input data that are given by the values of a so called
stratification variable. This page allows to create such a
variable in a convinient way.
The user has to specify at least two variables in the select field
labelled
Select variables to generate a stratification variable.
From this field, all variables that are available in the micro data set
can possibly be selected. Once two or more variables are selected, two
new inputs appear. The first one appears right next to the variable
selection field and is called
Specify variable name for stratification variable. In this
field, user can enter a desired name for the new variable. By default,
the variable name listed consists of the selected variables chained
together using _ as separator. By clicking into the field,
the user can enter a customized variable name. If the current value in
this text input field is not the name of an already existing variable, a
button termed Create stratification variable appears below.
Clicking on this button adds the new variable to the input data set. The
variable is generated as a factor variable in which the values of the
contributing variables are also chained together using _ as
the separator. After pressing the button, the page changes to the explore variable page where the newly
generated variable is already selected.
NAIn this section, users can set values in some variables to missing
(NA). The first step is to choose one of two possible
methods by choosing either by record ID (the default
selection) or by value in the radio button input labelled
How do you want to select the cells to be recoded to missing?
on top of the page.
If by record ID has been selected, the user can set
values in one or more variables for a specific record to missing. He
therefore needs to select at least one variable in the input called
Select variable. Once at least one variable has been
selected, a new input field occurs. In this input called
Select record ID, a number between 1 and at most the number
of records can be specified. This selection refers to the row in which
for the selected variables the values will be set to NA.
The user can change this index either by clicking on the small arrows at
the right hand side of the input field which allow to increment or
decrement the current number by one. As an alternative, it is also
possible to directly enter a number in the field.
In case by value has been selected, these choices are
slightly different. In the variable selection it is only possible to
select one variable. The other difference is that there is no input
field where the user can select a number. Instead, there is a dropdown
field where the user can select one of the distinct values of the
selected variable. The idea is that all records having this values in
the selected variable will the set to NA.
The remaining part of this page is identical for both choices of the
method. Below these inputs, an interactive table showing the current
microdata is shown. This table can be filtered and navigated exactly the
same way as already described here. If
all selections are valid, a button labelled
Set values to NA is shown above this table. Pressing this
button sets the correspondig values to NA in the micro
data. Afterwards, the page changes and the Exlore variables page is shown. In this
page, the (first) selected variable in which values should be set to
missing is pre-selected to be analyzed.
In this page, users find the functionality to deal with hierarchical data. The idea is as follows. Often data contain clusters, eg. individual within households or students within classes. In this case, it is often the case that some variables of the data set are only relevant on cluster-level while others are relevant on individual level. It is also often the case that one wants to apply different anonymization strategies for the different levels of the data. So the GUI offers a way to deal with this situation in the following way.
The radio buttons labelled What do you want to do? allow
to choose from
Prepare file for the anonymization of household level variables
(the default) and
Merge an anonymized household level file into the full dataset.
In the former case, the uploaded micro data set can be restricted to
those variables relevant for the cluster-level only. Once that has been
done, the user may anonymize the household file and can finally export
the anonymized file to disk as described here. In the latter case, an already exported,
anonymized household-level file can be imported to the GUI and merged
with the individual level file. Then the anonymization process can be
started by creating a problem instance using individual-level variables
as keys. Finally, the user is able to export an anonymized file that is
safe on both levels. We now describe both possibilities in this
section:
Prepare file for the anonymization of household level variables
In case the goal is to prepare a household-level file, the user first
needs to select an identifier for the households or clusters. This
select input termed Select the household id variable is
initially empty. Once a variable has been selected from the list of all
variables available from the input data, an additional select field
called
Please select all variables that refer to households and not to individuals
appears next to it. In this input, one or more variables that are
relevant for households only (that means, these variables feature the
contain the same values for each household) can be selected. Once at
least one additional variable has been chosen, a button labelled
Create household-input data is shown below. Clicking on
this button restricts the current data set to the selected variables and
to only one (the first) record for each value of the cluster
identificator. Finally, the page refreshes and the number of
observations and variables in the updated, household level data set is
shown. Additionally, the names of the variables as well as their type
are presented in tabular format.
Merge an anonymized household level file into the full dataset
In this case, the goal is to merge an already anonymized household
level file to the currently available data file. This procedure is
performed in two steps. In the first step, the user needs to click on
the Browse button to select the anonymized file that should
be merged. Once the Open button is clicked the file is
uploaded immediately. We note here that it is only possible to upload
data that have been exported as .rdata-files as described
here. After pressing the button, the file
will be uploaded to the system. If an error occured (e.g the selected
data file does not contain a data frame or if the data set does not
contain any variables that overlap with the current inputdata) an error
message is shown and the user may upload a different file from disk.
Once the file is successfully uploaded, the layout of the page
changes too. A button labelled
Reset uploaded household data appears which allows to reset
the household level data and makes it possible to upload yet another
file. Additionally, a dropdown field labelled
Select a variable containing household ids appears. In this
input, the user needs to select a variable from the list of variables
that are available from both datasets containing the identification
variable. The selected variable will finally be used to merge the
datasets. Below this selection, a button termed
Merge household- and individual level data is shown. On
clicking this button, the merge is performed. If everything went well,
the page refreshes and the number of observations and variables in the
updated micro data file shown. Additionally, the names of the variables
as well as their type are presented in tabular format.
This page is relevant for creating an sdc problem (of class
sdcMicroObj) that can then be anonymized within the GUI. If
the user navigates to this page and no inputdata have been uploaded,
this page shows two options. The user can either click on the button
labelled Upload microdata or on a button labelled
Upload a previously saved sdc problem. In the first case
the user is taken to the Microdata page where
microdata can be uploaded as described here, in the latter case the user is taken
to the Undo page where an already exported problem instance can be
uploaded.
If microdata are available and no problem has been defined, the user can define a new sdc problem instance. Details on how this can be done are given in chapter Set up a problem. Once a problem instance has been defined, the page layout changes and the user can either view or modify the problem instance as described here or apply anonymization techniques to categorical or continuous variables. Details on how specific methods can be applied are given in chapter Anonymization methods.
If microdata have already been uploaded, the first step in the
anonymization procedure is to create an sdc problem which can be done on
this page. The layout of this page is split into two parts. On the left
hand side, the user is presented with a table and choices that are
required to define a new problem instance. On the right hand side, the
user is given the possibility to explore a variable. This is useful for
example, to decide which variables should be used as categorical or
numerical key variables. For further discussion on the choice of
variables, the user should have a look at ?createSdcObj
which is the underlying function that is used to generate a problem. We
now explain in detail how to proceed.
On top of the right hand sidebar the user is shown a select input
field labelled Explore variables in which any of the
variables from the current micro data set can be selected. By default
the first variable of the data set is chosen. Below this select input a
graph depending on the variable type is shown. If the selected variable
is either of type factor, character or
integer (with less or equal than 10 unique values), a
barplot is shown. For variables of type numeric or
integer with more than 10 unique values, a histogram
showing the distribution is plotted. Below the plot, the number of
unique values including missing (NA) is shown. Finally,
below this information even more information on the selected variable is
shown. In case of a continuous variable, the typical main summary
statistics (Minimum, Mean, Median, Maximum and 5%-, 25%-, 75%- and
95%-quantiles) are presented while for factor variables (as well as
integer variables with not more than 10 unique values), the number of
occurences for each possible level is shown.
On the left hand side, an interactive table with a row for each variable available in the microdata and a total of 9 columns is shown. This table allows the user to specify relevant variables for the sdc problem. Also, it shows additional information on each variable. The variables are:
class()no (the default), Cat. and Cont.
no: the variable is not uses as either categorical or
continuous key variableCat.: the variable is used as categorical key
variableCont.: the variable is used as continuous key
variableFor columns PRAM, Weight,
Hierarchical identifier and Delete, checkboxes
are present in the table. These checkboxes are by default not selected.
The checkboxes can be enabled by clicking on them. While there can at
most be one variable selected as weight variable and variable holding
cluster ids, multiple variables may be checked in column
Remove or Pram.
Below the table, two slider inputs are shown. The first one, labelled
Parameter "alpha" is relevant for the frequency calculation
given the categorical key variables that contain missing
(NA) values. For this input, values between 0
and 1 (the default setting) in steps of 0.01
can be selected. We note that leaving the value at 1 leads
to the same results as in versions of sdcMicro <= 4.7.0.
For details on this parameter, please have a look at
?freqCalc. The second slider, termed
Parameter "seed" can take on values between
-250 and 250 in steps of 1 and is
the number used to set the seed for the random number generator to
ensure reproducability. By default, this value is set to 0.
We note that once the sliders are selected, values can also be increased
and decreased by clicking the up and down (or left and right) keys on
the keyboard.
Whenever values in either the radio buttons or the checkboxes are
changed, it is internally checked if all conditions for a successful
generation of a new sdc problem are fulfilled. In the case that some
restrictions are violated, either a popup window containing additional
information occurs or a red button with the error message is shown. The
user can then change the variable settings in the table. Once all checks
are passed, a blue button labelled Setup SDC problem
appears below the two sliders. Clicking on this button creates the sdc
problem. Finally, the page refreshes and the layout changes. In Anonymization Methods, these changes
are further explained.
Once an sdc problem has been defined as described above, the layout of the page changes. It now features a left sidebar and the main content is shown at the right side of the screen.
In the left sidebar, users can choose which kind of anonymization
options they want to apply. At the bottom of the sidebar, a button
labelled Reset SDC problem is shown. Clicking this button
allows to reset the current sdc problem. However, clicking this button
does not immediately reset the problem but instead, a popup window
appears. In this window the user has to confirm that the current problem
should be deleted. This action will be performed, if the user clicks on
the button labelled Delete current problem. If the user
clicks Dismiss, the sdc problem remains unchanged.
Below this button, several action buttons are shown which are
organized in sections Reset the Problem,
View/Analyze existing sdcProblem,
Anonymize categorical variables and
Anonymize numerical variables. By default, the first entry
Show summary in
View/Analyze existing sdcProblem is selected which is made
clear due to a different color of the button. The content in the center
of the screen is dependent on the choices in the left sidebar. We now
continue to describe the possible choices for View/Analyze existing
sdcProblem, Anonymize categorical
variables and Anonymize numerical
variables.
In case anything different from Show summary is
selected, the layout of the page is changed again. In this case, a
sidebar on the right hand side of the page appears in which many useful
statistics on the current anonymization process are listed below each
other:
In the first section, the “important” variables in the
current sdc problem are listed. These are the categorical key variables,
the numerical key variables (if any) as well as the variables defining
sampling weights or cluster identification (if any). For the categorical
key variables, the number of suppressions due to establishing
k-anonymity is also listed in this table.
The second block lists - also in tabular format - the number of
records in the data set within the current sdc problem instance and the
value for paramters alpha and random seed that
were used when setting up the current problem.
The next table shows the number and percentages of records violating
2-, 3- and 5-anonymity in the
current sdc problem. In parenthesis the corresponding numbers are shown
for the initial sdc problem without any anonymization procedures
applied.
In the case that variables have been specified as numerical key
variables when setting up the sdc
problem, another table showing the estimated minimal and maximum
risk for numeric key variables is shown for both the original and the
(possibly) modified variables. For more information have a look at
?dRisk.
The section on information loss is also only displayed if continuous
key variables are available in the current problem instance. If this is
the case, the values for utility measures IL1s and the
Difference of Eigenvalues are shown for both the original
and (possibly) modified variables. For more information have a look at
?dUtility.
This sidebar is always updated whenever the sdc problem instance is modified which is the case when any anonymization procedure was applied. In some cases it is also extended, for example when categorical variables have been post randomized as explained in here and here.
This page allows the user to view the current anonymization state. In the Show summary page, a lot of detailed information about the current problem instance is shown. After applying anonymization techniques, the GUI often changes to this page so that it is easily possible to check what has changed.
Furthermore, it is also possible to explore the variables within the current problem or to modify the problem instance by linking variables to some categorical key variables as described or to create random identification variables as it is described here.
This page gives an overview of the current sdc problem. The information listed here is dynamic and is updated whenever an operation (or rather, anonymization technique) has been applied to the problem instance. The summary of the problem is divided into the following subsections. However, not all of these sections are present at any time. The content of the possible parts will be explained in this chapter.
Summary of dataset and variable selectionIn this section, information about the dimension (number of records, number of variables) of the current data set in the active problem instance is shown. Additionally, the important variables in the sdcMicro are listed. These variables are:
We note that only the first entry (Categorical key variables) is always visible. The other entries are only shown when they were specified when the sdc problem was created as described here.
Computation timeThis section prints the current time spent on computations. This refers to the time that was actually spent performing anonymization steps as well as setting up the problem instance. The time shown here does however not track the time that was spent in the GUI.
Information on categorical key variablesIn this part of the summary, some aggregation statistics on the
categorical key variables are printed in tabular format. The table holds
4 columns and features a row for each categorical key
variable of the current problem instance. The columns of this table
are:
>0The the last three columns, the same information based on the data
set that was used to create the problem instance is shown in
parenthesis. We note that NA values (missings) are counted
as separate categories in this table.
Risk measures for categorical variablesIn the section, the expected number and percentage of
re-identifications in the population given the current set of
categorical key variables taking account possibly specified sampling
weights is printed. Furthermore, a robust measure is shown listing the
number of observations whose individual risk is larger than the median
of the individual risk distribution plus two times its “Median
Absolute Deviation”, for details have a look at
?mad.
The same information is also listed for the initial data set that was used to create the current sdc problem.
Information on k-anonymityIn this section, a table showing the number and percentages of
observations that violate k-anonymity is shown. The table
has the following 3 columns:
kk-anonymity in the current (anonymized) datak-anonymity in the initial data set used to set
up the problem instance.This table changes for example, if categorical key variables are recoded, k-anonymity is established, postrandomization has been applied (which is described here and here) or values based in their individual risk value are suppressed.
PRAMIn case, variables have been postrandomized, as described here and here, the transition matrices as are shown for each variable that has been post-randomized.
At the end of this section, a table with three columns summarizing the postrandomization results is printed. The columns are:
For each variable that has been postrandomized, a row is added to this table.
Compare numVarsIn this section, a table showing important statistics of numerical key variables is printed. However, this section is only shown if at least one variable has been specified as numerical key variable when setting up the current problem.
In case it is shown, the table has the following 8
columns.
modified) or the
initial data used to create the sdc problem (orig)This table is updated, whenever a numeric anonymization technique is applied on at least one numeric key variable.
Risk measures for numerical key variablesThis part shows a global risk measure based on the numeric key variables for the current and the initial data set. This information is only visible if at least one variable has been specified as numerical key variable when setting up the current problem.
The assumtion is that the re-identification risk based on numerical
key variables is initially always between 0% and
100%. The more the numeric key variables are changed, the
less the upper bound of this risk interval is.
Information lossThe section on information loss (data utility) of numeric key
variables is also only visible if numeric key variables are available in
the current problem instance. If this is the case, the values of two
measures, IL1s and the
difference of eigenvalues are printed for the current,
possibly modified numerical key variables as well as the initial data
set used when the problem instance was created. For details on the
measures, have a look at ?dataUtility.
Anonymization stepsAt the bottom of this page, the anonymization steps that have been applied, are listed. This helps the user to get a quick overview, on what has already been done to protect the data. This section is expecially useful when previously exported problem instances are imported. If no techniques have been applied, this information is also returned.
This view allows users to explore all variables in their current state in the sdc problem. The functionality is exactly the same as it was already described in Explore variables for the exploration of variables in the originally uploaded micro data set. The only difference being that the analyzed variables are now those currently available in the active problem instance.
Here, users can link one or more variables to a specific categorical key variable. For any linked variable, the anonymized dataset will feature the same suppression pattern than the key variable. This is helpful if for example, similar variables exist but it would not make sense to all add of them as categorical key variables.
In order to link a variable to a key variable, one has to select the
key variable using a drop-down menu field labelled
Select categorical key variable. Next to this input there
is another ‘select input’ field where all variables that are not used as
either categorical or numerical key variables, weight- or stratification
variable can be selected to be linked to the key variable before. In
this input field, multiple variables may be selected.
Once at least one variable has been selected for linking, a button
labelled Add linked variables appears at the bottom of the
page. Pressing this button adds the link to the current sdc problem and
the view refreshes to the Show summary
page. This information is also displayed on top of the page in the
section Important variables and information.
In this part of the GUI it is possible create a new random variable.
To perform the task, the user needs to specify two inputs. In the first
one, termed Specify name for the new ID variable, the
desired variable name of the new id needs to be entered. The second
input is a drop down field, in which either none (the
default value) or any variable available from the current sdc problem
may be selected. In case a variable has been selected in this input, the
newly generated variable features identical (but random) numbers for
equal values of the selected variable.
When both inputs have been chosen, a button labelled
Add new ID variable appears at the bottom of the page.
Pressing this button creates the new variable and adds it to the current
sdc problem. The view finally updates and the Show summary page is shown where the
dimension of the data set have been updated.
If Anonymize categorical variables has been selected in
What do you want to do? in the left sidebar of the screen,
the options Recoding, k-Anonymity, PRAM (simple),
PRAM (expert) and Supress values with high risk are available
from the radio button list termed Choose a Method and will
be described below.
This page allows to recode or reduce the level of detail in the
selected categorical key variables. The functionality is the same as
already described here for
recoding of factor variables in the original microsdcdata file. There
are two slight differences, though. The first one is that the variables
that can be selected in the input field termed
Choose factor variable are restricted to the categorical
key variables that have been chosen when the sdc problem was created, as
described here. The other difference is
that once recoding is done, the page refreshes and the content in the
right sidebar is recalculated. This especially affects the number of
observations violating k-anonymity that are shown in the block
k-anonymity.
This section allows to generate k-anonymity in the
categorical key vars or (independently) within subsets of the key
variables. This is done by setting specific values in the categorical
key variables to NA. Thus, for this method the parameter
alpha that has been specified during the creating of the sdc problem is of great
importance. For a discussion on this parameter, the reader is advised to
read the help pages for ?freqCalc. A feature for this
algorithm is that users may enter a preference specifying an order in
which variables the required suppressions should take place. Furthermore
it is possible to apply the method independently on groups defined by a
stratification variable. This is also the first choice the user has to
make on this page. In the select input field labelled
Do you want to apply the method for each group defined by the selected variable?
it is possible to select a variable from the set of all variables of
type factor, integer or character
excluding those variables that have been specified as categorical key
variables.
The next input field is termed
Do you want to modify importance of key variables for suppression?.
These radio buttons have two possible choices, No (the
default) and Yes. If No is selected, the
importance of variables is internally calculated in a way that the more
unique values a key variable has, the more likely it is that
suppressions in this variable will be done. If Yes is
selected by clicking on the radio button, the number of additional
select input fields appear below. These fields are dynamically labelled
Select the importance for key variable "{var}" where
{var} is a placeholder for any categorical key variable. In
each of the select inputs, a number between 1 and
n (the number of key variables) has be be selected. The key
variable that has importance 1 will typically have the
least additional suppressed cells while the variable where importance
equals n will very likely have the largest number of
introduced missing values.
Typically, all key variables will be used to determine if
k-anonymity is reached. If the number of key variables is
very large, it is sometimes helpful to establish
k-anonymity within subsets of the available key variables.
If the radio buttons labelled
Apply k-anonymity to subsets of key variables? is set to
No (the default choice), all key variables will be used to
determine k-anonymity. In this case, the user needs to
specify the required parameter k using a slider input
termed Please specify the k-anonymity parameter. This
slider has by default the value 2 and can take values
between 2 and 50.
If the choice for
Apply k-anonymity to subsets of key variables? is
Yes, additional elements appear below. Specifically, for
values from 1 to the number of key variables, two
additional inputs appear next to each other. The first one is a radio
button input field labelled
Apply k-anon to all subsets of {n} key variables? which is
by default set to No. If it is set to yes,
k-anonymity will be established in all combinations of the
categorical key variables containing n variables. The
second parameter is a slider input termed
k-Anonymity-parameter for {n} combs, which allows to set
the parameter k for this specific combination. For further
details on establishing k-anonymity in combination of key
variables, please have a look at ?kAnon.
Once all settings have been applied, a button labelled
Establish k-anonymity is shown on the bottom of the page.
Clicking this button starts the process to establish k-anonymity which
might take a long time. On the bottom right screen, a progress bar
occurs showing that the process is running. Once it is finished, the
page refreshes and the right sidebar is updated. Users should especially
have a look at the first table, where the number of suppressions within
each key variable is shown. Also, the section k-anonymity
is updated.
This page offers the possibility to randomize one or more variables
based on an invariant probability transition matrix. To apply this
method to the current sdc problem, the user has to choose at least one
variable from the input field labelled
Select variable(s) for PRAM. By default, no variable is
selected in this field. The user can select input from a set of
variables previously declared suitable for postrandomization. PRAM
variables have to be declared while setting up the problem instance in
the Anonymize tab.
Once at least one variable that should be pramed has been selected,
is it also possible to select a variable which will be used for
stratification, from the field named
Postrandomize within different groups (stratification)?. If
the default value of no stratification is changed, the post
randomization of the selected variables is performed independently for
each unique value of the selected variable. In this field, only one
variable may be selected. It should be noted, that stratification
variables can be created before setting up the sdc problem instance as
it was described here.
To create the transition matrix, two parameters (pd and
alpha) need to be provided using slider inputs.
pd refers to the minimum diagonal values in the
(internally) generated transition matrix. The higher the value chosen,
the more likely it is that a value stays in the same category and
remains unchanged. Parameter alpha allows to add some
perturbation to the calculated transition matrix. The lower this number
is, the less perturbed the matrix will get. By default, the value of
Choose value for 'pd' will be 0.8 and the
value of Choose value for 'alpha' will be 0.5.
For further details, have a look at ?pram.
After selecting at least one PRAM variable, a button labelled
Postrandomize appears at the bottom of the page. Pressing
this button performs the postrandomization. Afterwards, the page
refreshes and in the right sidebar a section called
PRAM summary either appears or is extended. In this part of
the sidebar, for each variable that has been postrandomized the number
and percentages of value changes are listed.
This page offers the possibility to randomize a variable using a
freely specified transition matrix. To apply this method to the current
sdc problem, the user has to choose one variable from the input field
labelled Select variable for PRAM in which by default the
first possible variable is selected. The user can choose in this input
from any variable that has been specified as a possible variable for
postrandomization during the initialization of the sdc problem and that
has not yet been pramed in the current sdc problem.
After selecting at least one variable, is it possible to select a
variable which will be used for stratification. If, in the input field
Postrandomize within different groups (stratification)?,
the default value of no stratification is changed, the post
randomization of the selected variables is performed independently for
each unique value of the selected variable. In this select field, only
one variable may be selected. It should be noted, that stratification
variables can be created before setting up the sdc problem instance as
it was described here.
Below these input fields, an interactive table is shown. This table
has to be edited by the user in a way so that it can be used as a
transition matrix. For any given row, the numbers specify percentages
that the current value (the actual row name) changes to the value
specified by the respective colum name. By default, in the diagonal of
the table, the values are 100. This means that the
probability that the value does not change is 100%. The
user can change the table in a way that the sum of the values in each
row equals 100. If this is not the case, a red button
appears below the table giving instant feedback that the table needs to
be further edited. Values in specific cells may be changed by clicking
into the cell and entering a new values.
Once the transition matrix is valid (eg. the values in all rows sum
up to 100), a button labelled Postrandomize
appears at the bottom of the page. Pressing this button performs the
postrandomization. Afterwards, the page refreshes and in the right
sidebar a section called PRAM summary either appears or is
extended. In this part of the sidebar, for each variable that has been
postrandomized the number and percentages of value changes are
listed.
On this page the user can set values for the most-risky records to
NA in a categorical key variable. To do so, the user needs
to select a categorical key variable from the select input field
labelled Select key variable for suppression. By default,
the first key variable is already selected. The next step is to set an
appropriate threshold value which will be used to identify the
“risky” records. These records are defined as those having an
individual re-identification risk larger than the selected threshold.
The threshold may be changed by updating the slider input termed
Threshold for individual risk. The range of this slider
starts at 0 and the maximum value depends on the current
sdc problem.
Below these input fields, a histogram showing the distribution of the
individual risk values is plotted. In the graph, a vertical black line
representing the current value of the threshold is also shown. Finally
there is a button labelled
Suppress {nr} values with high risk in variable {var}. The
labelling of this button is dynamic. It shows the number of records that
would be set to missing in the selected variable for the current choice
of the threshold. If this button is pressed, records in the selected
variable whose individual risks are above the threshold are set to
NA. The view finally updates and the Show summary page is shown where all
measures have been recalculated.
If Anonymize numerical variables has been selected in
What do you want to do? in the left sidebar of the screen
in the Anonymize page, the options Top/bottom coding, Microaggregation, Adding Noise and Rank
Swapping become available from the radio button list termed
Choose a Method. These methods will be described in the
subsequent chapters. We note however that only the first choice (Top/bottom coding) is always available. The
remaining choices are only visible if numeric key variables are
specified when creating the sdc problem as described here.
This page allows to replace values above (“Top coding”) or
below (“Bottom coding”) a threshold with a custom number. This
page not only allows to recode numeric key variables, but any numeric
variables currently available. The first step is to choose a variable
from the select input labelled Select variable. By default,
the first numeric variable in the current sdc problem instance is
selected. Next to this field are radio buttons labelled
Apply top/bottom coding? in which by default the value
top is chosen.
Below these input fields, the user is required to enter two numbers
in the input fields labelled Threshold value and
Replacement Value. These numbers relate to the threshold
(larger than in case of top coding and less than in case of
bottom-coding) for the first input and the number that will replace the
current values in the selected variable. To help users find suitable
thresholds, a boxplot showing the distribution of the currently selected
variable is shown below the inputs.
Once all required input - especially the threshold and the
replacement values - is set up and found to be valid, additional
elements appear between the input fields and the boxplot. The first
additional element is a text stating how many of the values would be
replaced as well as the corresponding percentage. Below this
information, a button labeled Apply top/bottom coding
appears. Once this button is pressed, the values are replaced according
to the current setting and the page updates with the additional elements
disappearing again and the boxplot is updated too. Also, the right
sidebar is updated. In case the recoded variable was a numeric key
variable, the values in sections
Risk in numerical key variables and
Information loss may change.
On this page it is possible to apply microaggregation to numeric
(key) variables of the current sdc problem. The user has the choice
among a total of 12 different methods. For details on the
specific methods, the user is referred to the manual of
?microaggregation.
The layout of this page changes depending on the specific method that
is selected. Microaggregation methods can broadly be categorized into
two categories, cluster-based and non-cluster based. This is also the
first selection the user can make on this page. Using the radio buttons
labelled Use a cluster-based method?, the choices
no (default) and yes can be selected by
clicking on the appropriate button. The choice in this input field
changes the possible selections in the select field termed
Select the method that is shown next to it. If
Use a cluster-based method? is no, the
following methods can be selected:
mdavrmdsimplesingleonedimspcamcdpcapppcaIf Use a cluster-based method? is yes, the
following choices are possible:
influenceclustpcaclustmcdpcaclustpppcaThe next choice the user can make is whether the microaggregation
should be performed on the entire data set of independently on groups
that are defined by the unique values of a stratification variable. By
default, the value no stratification is pre-selected in the
select field labelled
Apply microaggregation in groups (stratification)?. The
possible variables include all non-numeric variables that are available
in the sdc problem. We mention again that stratification variables can
be created before setting up the sdc problem instance as described here.
Finally, there are two additional input fields that appear for any of
the the microaggregation methods. The first one, labeled
Aggregation-level is a slider input that defines the size
of the groups that should be formed. The value of the slider is by
default 3 and it ranges from 1 to
15. The other input is labelled
Select Variables for Microaggregation. In this input, the
numeric variables that should be microaggregated can be selected from
the list of the numeric key variables. If it is empty, all variables
will be used. A tooltip once the user hovers over this input field also
informs that by default all numeric key variables will be
microaggregated.
For some specific methods, additional inputs appear below. For
non-clusterbased methods, there are two additional inputs labeled
Aggregation statistics and Trimming-percentage
shown below the variable selection input. The input called
Aggregation statistics is a list of radio buttons with the
choices mean (the default), median,
trim and onestep. If trim is
selected, a trimmed mean using the value from the slider input labeled
Trimming-percentage is calculated within each group and
this value is used to replace individual values. These additional
elements appear for methods simple, onedims,
pca, mcdpca and pppca. For method
simple a third additional element termed
Select variable for sorting appears. In this drop down
list, the user has to select a variable that will be used to sort the
data set before computing the required groups. For details, see
?microaggregation.
In the case that clusterbased methods should be used, the layout is
the same for any of the possible methods. The additional element appear
again below the variable selection input
Select variables for microaggregation. Users can select -
as described above - values for AggregationsStatistics and
the relevant Trimming-percentage if trim is
selected as the aggregation measure. Furthermore, users can select the
desired cluster method in a radio buttons input labeled
Clustermethod where the choices clara (the
default), pam, kmeans, cmeans and
bclust are possible. It is also possible to specify if the
data should be transformed before computing the clusters. In the radio
buttons list labeled Transformation, the choices
none (the default), log and
boxcox are possible. Finally, the desired number of
clusters that should be formed needs to be specified. This number can be
set in the slider input labeled Number of clusters. By
default it is set to 3.
If all options have been set, a button labeled
Perform Microaggregation is shown at the bottom of the
page. Clicking this button performs the microaggregation of the selected
variables according to the options that have been set. Since the
computation might take a long time, on the bottom right screen a
progress bar appears, showing that the process is running. Once it is
finished, the page updates and the Show
summary page is shown. On this page, the section
Compare numVars is either updated or added, and the
sections Information on risk for numerical key variables,
Information loss and Anonymization steps are
updated to display current values and statistics.
In this section it is possible to perturb numerical key variables by
adding stochastic noise. The first option is to select some numerical
key variables from the select input labeled
Select variables. If this input field is left empty (which
is the default), noise will be added to all numerical key variables.
Next to this field, users can select the desired algorithm in the
select input termed Select the algorithm. The choices
are:
additive (the default value)correlated2restrROMMoutdectcorrelatedWe note that the last method (correlated) is only
available if at least two numerical key variables are specified in the
current problem instance. For details on the methods, please refer to
the section ?addNoise on the main page.
Below these two input fields, a slider input is shown. This input is
dynamically labeled depending on the choice of the method. For all
methods, however, this slider is used to enter the amount of
perturbation which should be used. Since the parametrization for the
different methods is different, this slider has different default values
and different ranges depending on the choice of the method. Again, we
refer to ?addNoise for further details.
If all options have been set, a button labeled Add noise
is shown at the bottom of the page. Clicking this button adds noise to
the selected variables according to the options that have been set.
Since the computation might take some time, on the bottom right screen a
progress bar occurs showing that the process is running. Once it is
finished, the page updates and the Show
summary page is shown. On this page, the section
Compare numVars is either updated or added, and the
sections Information on risk for numerical key variables,
Information loss and Anonymization steps are
updated to display current values and statistics.
On this page, the user can apply rank swapping to numerical key
variables. For a complete description of the parameters, please see the
corresponding main page in sdcMicro,
?rankSwap.
A total of 6 inputs can be set. The first input, labeled
Select variables allows to select numerical key variables
for swapping. If this select field is empty (the default), all numerical
key variables will be used. The remaining inputs are all slider inputs
defining the required parameters for the algorithm as described in
rankSwap. The sliders
Percentage of lowest values that are grouped together before rank swapping
and
Percentage of largest values that are grouped together before rank swapping
refer to the top- and bottom- percentages that should be grouped
together before the method is applied. Both sliders have by default a
value of 0 (the minimum) and can take values up to
25.
The sliders Subset-mean preservation factor,
Multivariate preservation factor and
Rank range as percentage of total sample size. allow to
fine-tune the algorihm. The first slider refers to argument
K0, the second to argument R0 and the third
slider to argument P in rankSwap(). The
default values of these sliders are equal to the default values for the
function itself and can be changed within reasonable ranges. For details
on the impact of these parameters, please see
?rankSwap.
Once all options have been set, a button labeled
Apply rank swapping appears at the bottom of the page.
Clicking this button applies the algorithm on the selected variables
according to the options that have been set. Since the computation might
take some time, on the bottom right screen a progress bar appears,
showing that the process is running. Once the process is complete, the
page updates and the Show summary page
is shown. On this page, the section Compare numVars is
either updated or added, and the sections
Information on risk for numerical key variables,
Information loss and Anonymization steps are
updated to display current values and statistics.
On this tab it is possible to find out current values of various risk measures based on either categorical or numerical key variables in the active sdc problem. It is also possible to visualize or tabulate these variables as well as to identify “risky” records in anonymized data set.
If no problem instance has been specified, two buttons will appear.
Clicking on Create a SDC problem changes the view to the Anonymize page, where a new problem instance can
be generated. By pushing the button termed
Upload a previously saved problem, the view is changed to
the Undo page, where a previously saved problem
instance can be uploaded.
If a problem instance has been defined, this page features a three column layout. The left sidebar features the navigation that is divided into three section labeled Risk measures, Visualizations and Numerical risk measures. Specific measures can be selected by clicking on the action buttons shown in this sidebar. The current selected button is shown in a different color so that it is easy to see which selection is active.
On the right sidebar, two tables are shown. The first one, labeled
Variable selection lists the categorical and numerical key
variables. Additionally (if present), also the variables selected to be
possibly postrandomized as well as the variables holding sampling
weights or cluster ids are shown. The second table labeled
Additional parameters shows the number of records as well
as the choice of parameters random seed and
alpha that were used when the current problem was specified.
The main content depends on the current choice in the navgigation menu. In the following chapters, all possible selections are discussed.
In this section, it is possible to view current values of based on the categorical key variables, identify risky observations of compare plots of individual re-identification risks between original and anonymized micro data which is described here. Also, users may calculate suda2 and l-diversity risk measures.
Here, users can either obtain information on various risk measures based on the categorical key variables; identify risky records; or visualize the individual re-identification risks.
To select what information to view, the user needs to either select
Risk measures (the default value),
Risky observations or Plot of risks from the
radio button list labeled
What kind of results do you want to show?.
Risk Measures
Here, the number and percentages of observations that have a higher
indidvidual re-identification risk than the main part of the other
records is shown for both the initial as well as the anonymized data.
The individual re-identification risk is computed based on the selected
categorical key variables, and reflects both the frequencies of the keys
in the data and the individual sampling weights. A record is said to
have a re-identification risk different from the main part of the data
if its personal re-identification risk is either larger than the median
+ two times the Median Absolute Deviation of the distribution of all
individual risks (a robust measure) or if it is deemed large. For this,
the setting was choosen to be 0.1 (10%).
Also shown is the number (and corresponding percentages) of observations that are expected to be re-identified. This information is shown for the initial dataset as well as the anonymized data set so that comparisons can be easily done. In case a cluster-variable was specified during the setup of the problem instance, the expected number of re-identifications is also shown if the cluster (e.g persons living in households) information is taken into account as well.
Risky observations
This page allows to filter records in the anonymized data set,
depending on a threshold for the individual re-identification risk. To
do so, the user can select a specific threshold by moving the slider
input labeled Minimum risk for to be shown in the table.
The slider ranges from 0 (the default value) up to the
maximum risk-value currently available in the anonymized data set. If
the default value is not changed, all observations are marked as
“risky” because the re-identification risk is by default larger
than 0. Below the slider, the number and percentages of
observations with individual re-identification risks larger than the
currently specified threshold are shown. Below, a table containing the
categorical key variables, the numbers of fk,
Fk and the individual risk itself are shown for the
observations that are marked as “risky”. Once the value of the
threshold is changed, the number of risky observations will
decrease.
Plot of risks
On this page, two plots are presented. The first histogram shows the distribution of the individual re-identification risks in the anonymized data set, while the plot below it shows the same information based on the original data set that was used when the problem instance was created.
On this page, users can apply the SUDA algorithm. This algorithm can be used to search for Minimum Sample Uniques (MSU) in the data given the current set of key variables. The algorithm looks at those records that are unique in the sample (sample uniques), and checks if any of these sample uniques are also special uniques. Special uniques are defined as records having keys for which also a subset of the selected key variables is unique in the sample. See the help files for more information on SUDA scores.
We note that this algorithm can only be applied if the current
problem instance features three or more categorical key variables. If
this requirement is not fulfilled, this information is shown to the
user. Else, the user needs to choose a value for parameter
disFraction? which is the sampling fraction for the simple
random sampling or the common sampling fraction for stratified sampling
used within the algorithm. By default, this value is set to 0.01 and can
be changed by modifying the slider labeled
Specify the sampling fraction for the stratified sampling.
After pressing the button termed Calculate suda2-scores,
the actual computation is performed. Once computation is complete, the
layout of the page changes. On top of the page a button labeled
Reset to choose a different sampling fraction parameter.
Pressing this button resets the results and allows to recompute the
suda2 scores using a different value for parameter
disFraction. Below this button, two tables are shown. The
first table summarizes the suda2 scores that have been obtained. It
shows for 0 and 8 intervals the number of records having
suda2 scores of this value or within a specific interval. The second
table shows for each categorical key variable how much of the total risk
is contributed to by each of the variables. This amount is shown in the
second column (contribution) while the corresponding key
variable is listed in column variable.
Here you can compute the l-diversity of sensitive
variables. A dataset satisfies l-diversity if for every
combination of the categorical key variables there are at least
l different values for each of the sensitive variables. The
statistics refer to the value of l for each record. To
calculate this risk measure, the user needs to first select at least one
sensitive variable. This can be done in the input field
Select one or more sensitive variables where all variables
except for the categorical key variables can be selected. The other
choice is to set a value for the l-diversity constant which can
be done using the slider named
Select a value for the recursive constant. This constant is
used to determine if a record is unsafe. If the calculated value for
l-diversity for a record (having a specific key) is less
than this constant, it is said to violate l-diversity.
Once the parameters are set, a button labeled
Calculate l-diversity risk measure appears below. Pressing
this button forces the calculation of the measure using the selected
sensible variables and the constant. Once the calculation is finished,
the content of the page changes. At the top of the page, a button named
Reset to choose different input parameters is shown.
Pressing this button resets the results and allows to specify other
parameters. Below, a table containing for each selected sensible
variable the 5-number summary of the calculated l-diversity measure.
Below that, all the records that violate l-diversity based
on the choice of the recursive constant are displayed in an interactive
table. If all records are safe, no table is shown.
In this section it is possible to either compare current key
variables in the original and anonymized dataset graphically or in tabular format. It is also possible to
view measures of information loss
based on recoding of categorical key variables or show the number of
observations that violate
k-anonymity for arbitrary values of k.
On this page it is possible to graphically compare key variables
before and after the anonymization. In the select input labeled
Variable 1, the first categorical key variable is already
pre-selected while the value of the second input field
Variable 2 has the default value of none. If
only one variable is specified, users are presented with two graphs
below these inputs. First, we see a barplot of the original data as it
was when the problem instance was created. Below that, another barplot
showing the anonymized variable is shown.
If the value in Variable 2 is different from
none, the two graphs change. In this case a mosaicplot of
the two selected variables is shown for both the original and the
anonymized variables.
In this part of the interface it is possible to compare tabulations of categorical key variables before and after the anonymization. The page is built identically as the Barplot/Mosaicplot page. The only difference is that no graphs but tables are displayed below the input fields where the relevant variables can be selected. Also, the tables are shown next to each other to allow for easier comparison and less scrolling.
Recoding categorical key variables by combining levels leads to information loss. In this section it is possible to compare for each key variable, the effects of recoding. Thus, a table containing the following columns for each categorical key variable is shown:
The table is interactive and in case of many key variables, it can be sorted by clicking on the small arrow signs that are shown next to the column names.
On this page it is possible to find out how many records in the
anonymized dataset violate k-anonymity for different
choices of k. There is a slider input labeled
Select value for 'k' that can take values between
1 and 50. Dragging the slider with the mouse
or changing the value of the slider with the arrow-keys on the keyboard
leads to a recalculation of the number and percentage of observations
that violate k-anonymity for the current choice of
k. This information is printed on the screen below the
slider.
Furthermore, a table listing all the observations in the dataset that
violate k-anonymity is printed. For these observations, the
interactive table contains all categorical key variables. Here you can
browse the records that violate k-anonymity for the selected level of
k. All categorical key variables are shown as well as
risk (the individual risk), fk (the frequency
of the particular combination of key variables for each record in the
sample) and Fk (the estimated frequency of this combination
of key variables for each record in the population, taking sampling
weights into account) are shown.
This section provides information on important summary statistics of numerical key variables in the original and anonymized data; information on the current disclosure risk; as well as measures on information loss.
In this section the user can compare the distribution of the
numerical key variables in the current problem, between the original and
the anonymized data. The user can also calculate the available measures
given the label of a categorical key variable. To start, the user needs
to select a numerical key variable from the select input field labeled
Choose a numerical key variable. Only pre-selected
numerical key variables are available in this field. Next to this input
is another select input field labeled
Optionally choose a categorical variable. Its default value
is None. In this input field, users may select one of the
categorical key variables. If the default value is not changed, the
summary statistics shown in tabular form below are calculated for each
level of the specified categorical key variable.
Once these selections have been made, some important values are
printed in a section named Measures below. These values
include the Pearson correlation coefficient using pairwise complete
information, the standard deviations as well as the interquartile range
(a robust measure being the difference between 3rd and 1st quantile of
the data set) of the selected variable in the original and the
anonymized data set.
Below this information, two tables are presented. The first refers to
the original data and shows the Minimum, Mean, Median, Maximum as well
as the 5%-, 25%-, 75%- and 95%-quantiles of the selected numerical key
variable while the same information is shown in the table below for the
variable in the anonymized data set. In case that a categorical variable
has been choosen in
Optionally choose a categorical variable, these summary
statistics are calculated for each level of the selected categorical key
variable. We note that since the levels of the categorical key variables
might differ beetween original and anonymized data set, it is not
possible to show this information in a single table.
On this page, users can check on the estimated disclosure risk for
the selected numerical key variables. The measure can be interpreted in
the following way. In the original, unmodified data that has been used
to create the sdc problem, the risk for the numeric key variables is
assumed to be between 0% and 100%. The more
data anonymization techniques such as microaggregation or adding noise
are applied to the data, the less the upper bound of the risk will be.
So users can compare the estimated upper bound of the risk for numerical
key variables in the anonymized data and compare on how much it has
reduced from the initial value of 100%. We note that the
larger the deviations from the original data are, the lower the upper
risk bound will be. However, this has of course also an impact on data
utility measures that can be assessed from the menu button
Information loss as described below.
Here, users can check on two measures of information loss, IL1s and differences in eigenvalues. Generally speaking, the more the numerical key variables are modified (anonymized), the higher the information loss values for both measures. We also note that information loss and disclosure risk for numerical variables are always a trade of which need to be balanced.
Also shown on this page, are the values of the IL1s
measure (definition provided) as well as the differences of robust
eigenvalues of the data before and after the anonymization process.
In this tab, the GUI offers the possibiliby to export the current
state of the anonymized microdata from the current problem instance to a
file in various formats, and to save a report summarizing the
anonymization process as an html-file to disk. If no problem instance
has been specified, the user is informed of the need to create an sdc
problem first. By clicking on the button labeled
Create an SDC-Problem, the GUI changes to the Anonymize page, where the user can create a
problem. As an alternative, the user may upload a previously saved
problem instance. By clicking on the button
Upload a previously saved problem, the user is taken to the
Undo page where he may upload an previously saved
problem instance.
If, however, a problem instance has been defined, the page features a
sidebar on the left hand side of the screen. In this sidebar, the user
can click on one of two buttons, Anonymized Data (the
default) or Anonymization Report, by clicking on the
desired text or button. The active button is finally colored differently
and the content of the main page changed depending on your choice.
On this page, the microdata available at present in the active sdc problem instance after the applied anonymization techniques as described here can be saved to disk.
On top of this page, an interactive, sortable and browsable table containing the data that will be written to a file is shown. The variables can be sorted by clicking on the small arrows next to the variable names on top of the table. Also on the top, there is a dropdown field where users can select how many observations should be displayed on one page. On the bottom of the table, users find a dynamic pagination field which allows users to jump to a given “page” of the current table.
Below the table, two sets of radio buttons are shown:
Select file-format
Using this input, the desired output format can be specified. The
possible choices are R-Dataset, SPSS-File,
Comma-separated File, STATA-File and
SAS-File and can be selected by clicking on the appropriate
text or button. If Comma-separated File is chosen,
additional controls relevant for the generation of the output file
appear below. For this option, three additional radio button inputs are
available:
yes) which is the default setting or not
(no)Comma (the default),
Semicolon and TabDecimal point (the default)
and Decimal commaIf STATA-File is selected, an additional radio button
input is shown:
Randomize Order of Observations
This set of radio buttons allows to choose if the observations in the
dataset should be randomized. The possible choices are
No randomization (the default) and
Randomization at record level. In the case of the former,
the order of records remains unchanged. If
Randomization at record level is chosen, the records of the
dataset are randomly changed. In the case where a household/cluster
variable was selected when specifying the current sdc problem as
described here, two additional options
are possible. If Randomize by hierarchical identifier is
selected, the values of this identification variable are randomized
across the dataset. If the user opts to choose
Randomize by hierarchical identifier and within hierarchical units,
not only are the values of the household identification variable
randomly changed, the order of records within the households/clusters is
also permuted.
Below this option, a button labeled Save anonymized data
is shown. Clicking this button finally creates a file named
exportedData_sdcMicro_{timestamp}.{filetype} using
writeSafeFile() with the specified settings in the
destination folder that has been specified in the About page or (by default) in the current working
directory if this setting has not been changed.
On this page, an anonymization report can be generated and saved to
disk. The user can select the type of record that can be generated by
choosing from the radio button input. If
internal (detailed) (the default) is selected, a quite long
report is generated while the resulting report if
external (short overview) was selected just gives a very
broad overview about the anonymization process. Once the selection has
been done, clicking on the button labelled Save report
writes the report to disk. A file
sdcReport_internal_{timestamp}.html is generated in the
destination folder that has been specified in the About page or (by default) in the current working
directory if this setting has not been changed.
Only if you have uploaded microdata in dta-file format,
this action button appears. On this page you have the possibility to
edit variable labels in an interactive table. These modifications are
internally saved and added to the anonymized data file if and only if
you choose to export the file as a dta-file again as
described here.
In this tab, users find information to be able to reproduce the anonymization steps in the command line interface.
If no inputdata have been uploaded on the Microdata page, this page shows two
buttons. Clicking on the button labelled Upload microdata
sends the user to the Microdata section
of the GUI where microdata may be uploaded. Clicking on the button below
labelled Upload a previously saved problem navigates to the
Undo page where a problem instance that has been
saved to disk can be uploaded.
If inputdata or a problem instance are available, on the left hand
side of the page a sidebar is shown. In this sidebar, users can make the
choices that are described below by clicking on the appropriate buttons.
It is possible to View the current script (the default),
Import a previously exported sdcProblem from disk or
Export/Save the current sdcProblem to disk for later
re-import. This option is however only possible if a problem instance
has already been successfully specified.
On this page, users can view the code that has been applied so far.
This code could be run in sdcMicro directly with the only
limitation being that the file path when uploading microdata files is
relative to the fileInput()-functionality of shiny which
gives no way to return the path of the uploaded file on the local disk.
So for full reproducibiliby, users may need to adjust the path listed in
the current script.
Above the script output, a button labelled
Save Script to File is shown. Clicking on this button saves
the current script in a file
exportedScript_sdcMicro_{timestamp}.R in the destination
folder that has been specified in the About page or
(by default) in the current working directory if this setting has not
been changed.
On this page it is possible to import a previously saved problem
instance to the GUI. Once the user clicks on the Browse
button, they may locate any previously exported problem instance. The
file chooser only allows to upload .rdata files to minimize
possible mistakes. Once the file has been located and the
Open button is pressed, the selected file is loaded into
the GUI. If the import is successful, the content of the GUI is replaced
with the data from the imported file.
If the import was not successful, the user is presented with the
resulting error message and a button labeled Try again!.
After clicking this button, it is possible to upload a different file.
If the import of the problem instance works, the GUI changes to the
overview of the current sdc problem instance as described here.
This option is only shown in the sidebar once an sdc problem instance
has been generated as described here. If the
button labeled Save the current problem is clicked, the
entire current problem instance (and including all GUI-relevant data)
are saved to a file named exportedProblem_{timestamp}.rdata
in the destination folder that has been specified in the About page or (by default) in the current working
directory if this setting has not been changed.
After the file has been successfully saved, the page refreshes and shows the complete path to most recent saved file at the bottom of the page.
This page allows the user to undo the last anonymization step. If
there is no active sdc problem, the user is presented with two options.
The user can either click on the button labeled
Upload microdata in case no microdata have been uploaded to
the GUI, or on the button Create an SDC problem in case
that micro data are available. In the former case the page is changed to
the Microdata page while in the latter case it
is changed to the Anonymize page. In both cases
the user may also click on the Browse button to import a previously saved problem
instance. We note that this functionality is always available from
this page, independent on the availabilty of inputdata, an sdc problem
instance, or possibility to undo anonymization steps.
If a problem instance is available, the page layout changes. In case
it is possible to undo an anonymization step, the last anonymization
action that has been applied is printed on top of this page. Below there
is a button termed Undo last Step. Clicking on this button
opens a pop up window in which the user has to confirm that the last
anonymization step should be reverted. In case this button was
unintentionally pressed, clicking on Dismiss closes the
popup window and it is possible to continue with the anonymization
process.
Below, this button there is another action button labeled
Save current state which has exactly the same functionality
as the button described here.
Once the problem is successfully saved to disk, the page refreshes and
the path the exported file is shown.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.