The purpose of the sdcTable vignette is to show how to get up and running with sdcTable; for details, including a complete list of options, consult the help pages or the manual for the following main functions of the package:
makeProblem
using e.g: help('makeProblem')
or ?makeProblem
primarySuppression
using e.g:
help('primarySuppression')
or
?primarySuppression
protectTable
using e.g:
help('protectTable')
or ?protectTable
setInfo
using e.g: help('setInfo')
or
?setInfo
getInfo
using e.g: help('getInfo')
or
?getInfo
The main functions that are exported to users are shown in Figure 1.
Function makeProblem()
is used to create objects of
class sdcProblem
. Instances of class
sdcProblem
hold the entire information that is required to
perform primary or secondary cell suppression such as assumed to be
known upper and lower cell bounds or upper-, lower- or sliding
protection levels that are required to fulfill when solving the
secondary cell suppression problem. All this information can be modified
using function setInfo()
.
primarySuppression()
is applied to objects of class
sdcProblem
. By setting function parameters users can choose
and apply a pre-defined primary suppression rule. Using
setInfo()
, one can easily implement a custom primary
suppression rule, too.
Function protectTable()
is used to protect primary
sensitive table cells in objects of class sdcProblem
. A
successful run of function protectTable()
results in an
object of class safeObj
. Using getInfo()
one
can extract information from objects of such class, most importantly of
course a data set containing all table cells along with the suppression
pattern.
More detailed information on all the possibilities is available in the help-files, additional information is given in the corresponding sections of this vignette that deal with specific functions. The first step however to get started is to load the package, which can easily be done as shown below:
library(sdcTable)
packageVersion("sdcTable")
## [1] '0.32.5'
We now walk through the steps that are required to protect tabular data using sdcTable. In the first example we are going to protect table cells given a three-dimensional tabular structure with some sub-totals.
We will start by discussing input data sets in sections “Starting from microdata” and “Using aggregated data”. Then we continue by discussing how to define and describe dimensional variables in “Defining hierarchies” which is a crucial step in the entire procedure. Once the hierarchies are defined it is necessary to create suitable objects as described (here) that can be used to identify and suppress primary sensitive cells. This is shown in section “Identifying sensitive cells”. Finally we discuss how to “protect primary sensitive table cells”.
Throughout it is also shown how to set and extract information from
the objects we are working with using functions getInfo()
and setInfo()
.
In this example we suppose we have collected data from
1000
individuals. A subset of the available data is shown
below:
## V1 V2 V3 numVal2 numVal1
## A w d 49.93 71.72
## C m f 48.44 55.96
## Ba m a 43.20 64.69
## Bc w d 31.38 30.72
## Bc w a 49.46 54.93
## Ba m d 42.02 22.23
We note that the information we have obtained for any individual corresponds to exactly one row in the input data.frame. That is supposed to be available in R.
The micro data consist of 5
variables. The first 3
variables (V1
, V2
and V3
) are
categorical variables that will later define the table that needs to be
protected. Variables ‘numVal1’ and ‘numVal2’ correspond to arbitrary
variables containing some kind of information measured for each
individual.
To create the tabular structure that is required to protect any table cells within the table it is of course of interest to have a look at possible values or characteristics of the categorical variables that define the table.
V1
: this variable has a total of
6
codes without subtotals which are listed below:'A', 'Ba', 'Bb', 'Bc', 'C', 'D'
V2
: this variable has a total of
2
codes without subtotals which are listed below:'m', 'w'
V3
: this variable has a total of
6
codes without subtotals which are listed below:'a', 'b', 'c', 'd', 'e', 'f'
The step on how to define level-hierarchies that have to include all possible (sub)totals is explained below.
Using sdcTable it is also possible to start with a ‘complete’ dataset. This means that the input dataset already contains rows with all possible level-combinations that can occur. This also includes combinations with (sub)totals. In this case it is required that the input data contain a column holding cell counts. Using the example data already discussed in section “Starting from microdata”, the complete dataset could be specified as shown below:
## V1 V2 V3 Freq numVal1 numVal2
## 163 C Tot f 24 7523.30 7305.85
## 164 D Tot f 31 7723.40 7698.75
## 165 Tot m f 107 24033.37 24325.32
## 166 B m f 55 12797.11 13372.83
## 167 Tot w f 85 25082.25 25459.33
## 168 B w f 50 12600.68 12861.13
Even though we only show a small subset of the data it is immediately
clear that in object completeData
(sub)totals are listed.
These combinations can be calculated from the microdata by summation
over several codes in one or more dimensional variables. As in section
Starting from microdata it is of interest
which codes were specified for each dimensional variable. This
information is given below:
V1
: this variable has a total of
8
codes including all possible subtotals which are listed
below:'Tot', 'A', 'B', 'Ba', 'Bb', 'Bc', 'C', 'D'
V2
: this variable has a total of
3
codes including all possible subtotals which are listed
below:'Tot', 'm', 'w'
V3
: this variable has a total of
7
codes including all possible subtotals which are listed
below:'Tot', 'a', 'b', 'c', 'd', 'e', 'f'
We also note that in completeData
a variable
Freq
is available which gives information on the
corresponding cell counts. This means that for example a total of
50
individuals contribute to the table cell where variable
V1
equals B
, variable V2
is
w
and variable V3
is equal to
f
.
Whether or not one starts to work with micro data or already with a complete, pre-aggregated dataset the next step is always the definition of the hierarchies defining the tabular structure.
We could see here (for micro data) and here (for pre-aggregated data) that the set of
codes available in the input data for variables V1
,
V2
and V3
differ since in the case where micro
data are used as input data, no codes for subtotals are included in the
micro data while in the case where pre-aggregated data are used those
subtotals must already be included in the input data set.
When defining the complete hierarchies, no (sub)-totals must be excluded from the description. This means that for each variable defining one dimension of the table the complete structure must of course includes all (sub)totals.
In this example the hierarchies we want to define are quite basic. We
start by showing the level-codes for each variable V1
,
V2
and V3
that are included in
completeData
but not in microData
.
V1
:'Tot', 'B'
V2
:'Tot'
V3
:'Tot'
We observe that variable V1
has two codes
(Tot
and B
) that can be calculated from the
codes of V1
available in the micro data set
microData
. For variables V2
and
V3
only one total value (Tot
) exists which
means the summation over all characteristics of variables
V2
and V3
is the (only) total value. To
specify the complete structure of a dimensional variable one needs to
create a data frame or a matrix for each of those variables. The
structure of any object describing a dimensional variable be created as
follows:
@
While this may sound difficult, it is in fact quite easy to create
such objects within R. We will now explain how to
create the required objects for the dimensional variables
V1
, V2
and V3
used in the
example.
V1
The hierarchy we want to describe is as follows. The overall code
Tot
is calculated from the codes (A
,
B
, C
and D
). Additionally, code
B
(which is the second (sub)total-code for variable
V1
as described here) can be
calculated from the level-codes Ba
, Bb
and
Bc
.
Following rule 1, we have to create a data frame or matrix consisting of two columns, the first specifying levels, the second column the corresponding level codes. Since we have to follow a top-down approach, the first level code must always correspond to the grand total which is always considered as the code with a level equaling 1. Thus, we create the matrix with a single row defining the overall total as follows:
## [,1] [,2]
## [1,] "@" "Tot"
The level code for the overall total is @
because
according to rule 4 it is the only allowed character in the first column
and it consists of exactly 1 character. Also, since the overall total is
defined as level 1, the number of characters of the string
@
and the level of the overall total code Tot
matches.
The next step is to add additional codes. As mentioned before, codes
A
, B
, C
and D
contribute the the overall total. Therefore we know that these codes are
considered as level 2 codes and must be (according to the top-down
approach) listed below the overall total code. Adding these codes to
object dimV1
is shown below:
mat <- matrix(nrow = 4, ncol = 2)
mat[, 1] <- rep("@@", 4)
mat[, 2] <- LETTERS[1:4]
dimV1 <- rbind(dimV1, mat)
print(dimV1)
## [,1] [,2]
## [1,] "@" "Tot"
## [2,] "@@" "A"
## [3,] "@@" "B"
## [4,] "@@" "C"
## [5,] "@@" "D"
We know that code B
is a subtotal that can be calculated
from codes Ba
, Bb
and Bc
. Since
B
is a code of level 2, the codes contributing to it must
be of a lower level, in this case of level 3. We show below how to add
the codes to object dimV1
:
mat <- matrix(nrow = 3, ncol = 2)
mat[, 1] <- rep("@@@", 3)
mat[, 2] <- c("Ba", "Bb", "Bc")
dimV1 <- rbind(dimV1, mat)
print(dimV1)
## [,1] [,2]
## [1,] "@" "Tot"
## [2,] "@@" "A"
## [3,] "@@" "B"
## [4,] "@@" "C"
## [5,] "@@" "D"
## [6,] "@@@" "Ba"
## [7,] "@@@" "Bb"
## [8,] "@@@" "Bc"
Now object dimV1
contains all possible codes along with
their levels. However, it not valid because the top-down approach is
violated. This means that codes that contribute to a (sub)total must be
listed directly below it. If we would not change the order of object
dimV1
, sdcTable would assume that code
D
can be calculated by summation over codes
Ba
, Bb
and Bc
. For this reason it
is necessary to move this “block” up so that it is directly
below code B
. The required code and the resulting correct
object describing the structure of variable V1
is printed
below:
## [,1] [,2]
## [1,] "@" "Tot"
## [2,] "@@" "A"
## [3,] "@@" "B"
## [4,] "@@@" "Ba"
## [5,] "@@@" "Bb"
## [6,] "@@@" "Bc"
## [7,] "@@" "C"
## [8,] "@@" "D"
Using this information, sdcTable internally calculates all kinds of information on dimensional variables. So for example it is able to deal with codes that can be (temporarily) removed from the structure because it can be considered as a “duplicate”. This is however not the case for this basic dimensional variable that has a total of 8 codes of which 6 are required to calculate information for the 2 (sub)totals.
Since versions >= 0.27
, sdcTable
allows to use inputs created from package sdcHierarchies
as input. This package allows for a very simple way to create, compute
and modify hierarchies. For a complete introduction, the package
vignette can be viewed with hier_vignette()
. The main
functions are hier_create()
, hier_add()
,
hier_rename()
and hier_delete()
. We now show
an alternative way to generate the hierarchy for variable
V1
.
dimV1 <- sdcHierarchies::hier_create(root = "Tot", nodes = LETTERS[1:4])
dimV1 <- sdcHierarchies::hier_add(dimV1, root = "B", nodes = c("Ba","Bb","Bc"))
sdcHierarchies::hier_display(dimV1)
## Tot
## ├─A
## ├─B
## │ ├─Ba
## │ ├─Bb
## │ └─Bc
## ├─C
## └─D
sdcTable will internally convert the tree-based
structure generated using functionality from the sdcHierarchies
package automatically into the data.frame
based structure
discussed at the begin of this section.
V2
The creation of a suitable object that describes the hierarchical
structure of variable V2
is easy. We are only dealing with
one overall Total (Tot
) that is the sum of all codes listed
here for this variable.
The code how to specify an object that describes the structure of
dimensional variable V2
is given below:
dimV2 <- sdcHierarchies::hier_create(root = "Tot", nodes = c("m", "w"))
sdcHierarchies::hier_display(dimV2)
## Tot
## ├─m
## └─w
We see that the overall total (Tot
) is again listed in
the first row with the two other contributing codes (m
and
w
) being below in the same hierarchy level.
V3
The creation of a suitable object that describes the hierarchical
structure of variable V3
is easy. We are only dealing with
one overall Total (Tot
) that is the sum of all codes listed
here for variable V3
.
The required code to generate an object specifying the hierarchical
structure of variable V3
is given below:
dimV3 <- sdcHierarchies::hier_create(root = "Tot", nodes = letters[1:6])
sdcHierarchies::hier_display(dimV3)
## Tot
## ├─a
## ├─b
## ├─c
## ├─d
## ├─e
## └─f
It is required to create an object defining the complete structure and hierarchies for each dimensional variable. Once this step has been done, the multidimensional tabular structure that is required to apply any statistical disclosure methods can be created using makeProblem().
sdcProblem
for further
processing
We now show how to create objects of class sdcProblem
which can further be used to identify, suppress and protect sensitive
table cells.
It was discussed here and here how micro data and pre-aggregated data can
be used as data-input objects. We will now explain how to create
instances of class sdcProblem
from both
microData
and completeData
and describe the
required and optional parameters of function
makeProblem()
.
We start building a suitable object of class sdcProblem
starting with the data on individual level available from object
microData
.
dimList <- list(V1 = dimV1, V2 = dimV2, V3 = dimV3)
prob.microDat <- makeProblem(
data = microData,
dimList = dimList,
dimVarInd = 1:3,
freqVarInd = NULL,
numVarInd = 4:5,
weightInd = NULL,
sampWeightInd = NULL)
First we have to combine the objects describing the hierarchical
variables V1
, V2
and V3
into a
list-object named dimList
. Each list element is one of the
objects created in section “Defining
hierarchies”. The names of the list-elements must correspond to the
variable name that the corresponding list-element refers to. In this
case, the first list-element - dimV1
- should describe
variable V1
in the input data set microData
when calling makeProblem()
while the second list element -
dimV2
- defines the hierarchy of variable V2
and dimV3
- the third list element - describes the
structure of variable V3
.
The remaining parameters are quite self-explanatory and shorty described below:
data
: the data set that should be used, in this case
microData
dimList
: a named list containing information on the
structure of dimensional variables as described just abovedimVarInd
: the column indices of dimensional described
in dimList
.freqVarInd
: if not NULL
, an index
specifying the column that contains information on cell countsnumVarInd
: if not NULL
, an index
specifying the columns holding other numerical variablesweightInd
: if not NULL
, an index
specifying the column that contains info on weights that should be used
in the secondary cell suppression problem instead of cell countssampWeightInd
: if not NULL
, an index
specifying the column holding sampling weights for each
person/groupBuilding an object of class sdcProblem
using the
complete, pre-aggregated data completeData
as discussed above is very similar as it is shown below:
### problem from complete data ###
dimList <- list(V1 = dimV1, V2 = dimV2, V3 = dimV3)
prob.completeDat <- makeProblem(
data = completeData,
dimList = dimList,
dimVarInd = 1:3,
freqVarInd = 4,
numVarInd = 5:6,
weightInd = NULL,
sampWeightInd = NULL)
The only difference is that in this case we define parameter
‘freqVarInd’ that specifies a column within the input data set {}
containing information on cell counts. Also the indices of argument
numVarInd
are different to the first example.
In any case, both procedures return an object of class
sdcProblem
as it can easily be checked:
## [1] TRUE
We now can check if the cell counts of both objects are equal.
Function getInfo()
can be used to extract information from
objects of class sdcProblem
. Specifying argument
type
as freq
, getInfo()
returns
cell counts which are indeed equal independently if micro-data or
pre-aggregated data have been used as input to create the complete
tabular structure.
counts1 <- getInfo(prob.completeDat, type = "freq")
counts2 <- getInfo(prob.microDat, type = "freq")
all(counts1 == counts2)
## [1] TRUE
Once the problem has been set up and an instance of class {} is
available, it is possible to identify and suppress sensitive table cells
as we demonstrate in the next section using object
prob.completeDat
.
Identifying and suppressing primary sensitive cells is usually done
by applying function primarySuppression()
.
Having a look at the cell counts in table
prob.completeDat
shows that a total of 15
cells have less than 10 individuals contributing to it. We think that
these cells should be considered as primary sensitive and we want to
have them protected.
When creating an object of class sdcProblem
, all cells
are assigned an anonymization state. The possible codes are listed
below:
"u"
: cell is primary suppressed and needs to be
protected"x"
: cell has been secondary suppressed"s"
: cell can be published"z"
: cell must not be suppressedThe goal is now to change the anonymization status of all cells
having less than 10 individuals contributing to it from the default
value of s
to u
. The easiest way is to use
function primarySuppression()
directly:
prob.completeDat <- primarySuppression(prob.completeDat, type = "freq", maxN = 10)
Argument type
specifies the primary suppression rule we
want to apply. In this case we want to use the frequency threshold rule
that allows to suppress all table cells having cell counts less or equal
than the threshold specified using argument maxN
.
primarySuppression()
also allows to apply the nk-dominance
rule or the p-percent rule directly, in case micro data have been used
as input data. For all possible parameters and their explanation the
interested reader may consult the manual or the help-page of
primarySuppression()
.
After performing the suppression, we can have a look at the distribution of the anonymization states:
##
## s u
## 153 15
summary(prob.completeDat)
## The raw data contain pre-aggregated (tabular) data!
##
## The complete table to protect consists of 168 cells and has 3 spanning variables.
## The distribution of
## - primary unsafe (u)
## - secondary suppressed (x)
## - forced to publish (z) and
## - selectable for secondary suppression (s) cells is shown below:
##
## s u
## 153 15
##
## If this table is protected with heuristic methods, a total of 12 has (sub)tables must be considered!
One can see that the 15
cells having counts less or
equal than 10 have been identified and marked as primary suppressed.
However, we should note that it is very easy to implement custom
suppression rules by manually changing the anonymization state of cells
using functions setInfo()
or
changeCellStatus()
. Information on how to use these
functions is of course provided in the manual and the corresponding
help-pages.
To protect these cells by solving the secondary cell suppression
problem one can go on to use function protectTable()
as
explained in the next section.
sdcTable provides algorithms to protect primary
sensitive table cells defined in objects of class
sdcProblem
. The algorithms that may be selected are shown
below:
SSBtools::GaussSuppression()
.createArgusInput()
(?createArgusInput
) and
solve the problem using Argus.< 0.32
.We show how to protect the data using the available algorithms. For
an extensive discussion on the possible parameters have a look at the
manual or help page for function protectTable()
.
resGAUSS <- protectTable(prob.completeDat, method = "GAUSS")
resHITAS <- protectTable(prob.completeDat, method = "HITAS")
resOPT <- protectTable(prob.completeDat, method = "OPT")
resHYPER <- protectTable(prob.completeDat, method = "HYPERCUBE")
resSIMPLE <- protectTable(prob.completeDat, method = "SIMPLEHEURISTIC")
Having a look at the resulting objects we can observe that the number of secondary suppressions required to protect the 15 primary sensitive cells (by default against exact re-calculation given sliding protection levels of 1 for each primary sensitive cell) differs.
Using the “OPT”-algorithm, a total of 23
cells
have been marked as secondary suppressions. When using
“HITAS”-algorithm, it was required to additionally suppress
25
cells. A total of 27
cells was selected and
marked as secondary suppressions when the “HYPERCUBE” algorithm
was used while 27
additional suppressions were required for
the fast heuristic simple procedure “SIMPLEHEURISTIC” and
23
supps for the Gaussian elimination method.
One now easily get information from the resulting output objects that
are instances of class safeObj
by using function
getInfo()
or applying the summary
-method. For
the former we show how to extract the final data set which can be
achieved as follows:
## V1 V2 V3 Freq numVal1 numVal2 sdcStatus
## 1: Tot Tot Tot 1000 294693.72 298707.90 s
## 2: Tot Tot a 178 49115.62 49784.65 s
## 3: Tot Tot b 159 49115.62 49784.65 s
## 4: Tot Tot c 177 49115.62 49784.65 s
## 5: Tot Tot d 144 49115.62 49784.65 s
## 6: Tot Tot e 150 49115.62 49784.65 s
As we can see above the final result data set contains all columns
specified in the input data set along with another column
sdcStatus
that specifies the anonymization state for each
table cell.
For the latter we show how to apply the summary method. This can be done by applying the following code:
summary(resOPT)
##
## #####################################
## ### Summary of the protected data ###
## #####################################
## --> The input data have been protected using algorithm 'OPT'
## --> To protect 15 primary sensitive cells, 23 cells were additionally suppressed
## --> A total of 130 cells may be published
##
## ###################################
## ### Structure of protected data ###
## ###################################
## Classes 'safeObj', 'data.table' and 'data.frame': 168 obs. of 7 variables:
## $ V1 : chr "Tot" "Tot" "Tot" "Tot" ...
## $ V2 : chr "Tot" "Tot" "Tot" "Tot" ...
## $ V3 : chr "Tot" "a" "b" "c" ...
## $ Freq : num 1000 178 159 177 144 150 192 487 82 80 ...
## $ numVal1 : num 294694 49116 49116 49116 49116 ...
## $ numVal2 : num 298708 49785 49785 49785 49785 ...
## $ sdcStatus: chr "s" "s" "s" "s" ...
## - attr(*, ".internal.selfref")=<externalptr>
## NULL
We see that the summary provides all kind of useful information such as the algorithm that has been used to protect primary sensitive cells, the time it has been taken to solve the problem, the number of primary sensitive and secondary suppressed cells as well as the number of cells that may be published. Also, a excerpt of the final data set is shown.
I would also like to mention that an iterative algorithm is available
in function protectLinkedTables()
that allows to protect
two tables that have common table cells. The function takes two objects
of class sdcProblem
as input and a list defining the common
cells in both tables. Details on how to construct this a list-element
are given in the manual and help-page of
protectLinkedTables()
.
A lot of work has gone into the rewrite of sdcTable using S4-classes and methods in order to robustify the code and in order to make it easier in future to add new algorithms such as rounding- or cell-perturbation methods and features.
I would really like to hear any kind of feedback and will be more
than happy to work in patches you submit or ideas any one might have
which would make it easier to work sdcTable. Also, the
next step in the evolution of the package will be performance
optimization, evaluation for possibilities of parallel computing and so
on. I would really like to hear any kind of feedback on package users on
these kind of things. Thus, for any remarks, please do not hesitate to
contact me using my e-mail adress
bernhard.meindl@statistik.gv.at
.