Author: Bernhard Meindl
Date: 2025-09-03

# Introduction to the cellKey-Package

#### Bernhard Meindl

#### 2025-09-03

## About the cellKey package

This package implements methods to provide perturbation for statistical tables. The implementation if greatly inspired by the the paper Methodology for the Automatic Confidentialisation of Statistical Outputs from Remote Servers at the Australian Bureau of Statistics (Thompson, Broadfoot, Elazar). This approach however was generalized and a new specification on how record keys are specified and the lookup-tables are defined is used. This package makes usage of perturbation tables that can be defined in the ptable package. This document describes the usage of version `1.0.3` of the package.

## Main Features

In the cellKey package it is possible to pertub multidimensional count and magnitude tables. Functionality to generate suitable record keys is provided in function `ck_generate_rkeys()`. Using sha1-checksums on the input datasets, we can make sure that the same record keys are generated whenever the same input data set is used for which record keys should be generated. However, it is also possible of course to use already pre-generated record keys available as variable in the microdata set.

The package allows to make use of sampling weights. Thus, both weighted and unweighted count and magnitude tables can be perturbed. The hierarchical structure of the variables spanning the table can be arbitrarily complex. To generate these hierarchies, the cellKey packages depends on functionality available in package sdcHierarchies. Finally, auxiliary methods are provided that allow to extract valuable information from objects created by the main function of this package `perturbTable()`. For example `mod_counts()` returns a data object showing for each cell the perturbation value and where it was found in the lookup table.

What is left is of course identifying bugs and issues and also to optimize performance of the package as well as to include some real world examples, eg. census tables.

## An Example

We now show the capabilities of the cellKey package by running an example that

### Load the Package

    library(cellKey)
    packageVersion("cellKey")

    ## [1] '1.0.3'

The first step in this approach is to generate a statistical table, which can be achieved using `ck_setup`. This function generates an object that contains all the information required to perturb count- (and optionally continuously scaled) variables. There are however a few inputs that `ck_setup()` requires and that need to be generated beforehand. These inputs are:

- `x`: a `data.frame` or `data.table` containing microdata
- `rkey`: defines where to find or how to compute record keys. It is possible to either specify a scalar character name or a number. If a number is specified, record keys are internally created by sampling from a uniform distribution between 0 and 1 with the random numbers rounded to the specified value of digits. If a name is specified, this name is interpreted as a variable name within `x` already containing record keys.
- `dims`: a named list where each list element contains a hierarchy created with functionality from package sdcHierarchies and each name refers to a variable in `x`
- `w`: either `NULL` (no weights) or the name of a variable in `x` that contains weights
- `countvars`: an optional character vector of variables in `x` holding counts. In any case a special count variable `total` is internally generated that is `1` for each row of `x`
- `numvars`: an optional character vector of variables in `x` holding numerical variables that can later be perturbed.

We continue to show how to generate the required inputs. The first step is to prepare the inputdata.

### Specifying inputdata

The testdata set we are using in this example contains information on person-level along with sampling weights as well as some categorical and continuously scaled variables. We also compute some binary variables for which we also want to create perturbed tables.

    dat <- ck_create_testdata()
    dat <- dat[, c("sex", "age", "savings", "income", "sampling_weight")]
    dat[, cnt_highincome := ifelse(income >= 9000, 1, 0)]

We are now adding record keys (which will be referenced later) with 7 digits to the data set using function `ck_generate_rkeys` as shown below:

    dat$rkeys <- ck_generate_rkeys(dat = dat, nr_digits = 7)
    print(head(dat))

    ##       sex        age savings income sampling_weight cnt_highincome     rkeys
    ## 1:   male age_group3      12   5780              57              0 0.7552293
    ## 2: female age_group3      28   2530              48              0 0.3716140
    ## 3:   male age_group1     550   6920              67              0 0.9195144
    ## 4:   male age_group1     870   7960              48              0 0.4260026
    ## 5:   male age_group4      20   9030              55              1 0.5450293
    ## 6: female age_group3     102   3290              79              0 0.1694579

To ensure that the same record keys are computed each and every time for the same data set, a seed based on the sha1-hash of the input dataset is computed by default in `ck_generate_rkeys`. This seed is used before sampling the record keys and may be overwritten using the `seed` argument.

The goal of this introduction is to create a perturbed table of counts of variables `sex` by `age` for all observations as well as for the subgroups given by `cnt_highincome` that are non-zero. We also want to create perturbed tables of continously scaled variables `savings` and `income` also giving the hierarchical structure defined by `sex` by `age`.

### Specifying dimensions

It is required to define hierarchies for each of the classifying variables of the desired table. There are two ways how the hierarchical structure of these variables (including sub-totals) can be specified. One way is to use `"@; value` format as (also) used in sdcTable. We would suggest however, to use the second alternative which is using functionality from package sdcHierarchies. In this vignette, we only show the preferred way to generate hierarchies for categorical variables `age` and `sex` below:

    dim_sex <- hier_create(root = "Total", nodes = c("male", "female"))
    hier_display(dim_sex)

    ## Total
    ## ├─male
    ## └─female

For variable `age` the process is very much the same:

    dim_age <- hier_create(root = "Total", nodes = paste0("age_group", 1:6))
    hier_display(dim_age)

    ## Total
    ## ├─age_group1
    ## ├─age_group2
    ## ├─age_group3
    ## ├─age_group4
    ## ├─age_group5
    ## └─age_group6

The idea of sdcHierarchies is to create a tree object using `hier_create()` and then add (using `hier_add()`), delete (with `hier_delete()`) or rename (using `hier_rename()`) elements from this hierarchical structure. For more examples have a look at the package vignette of the package which can be accessed with `sdcHierarchies::hier_vignette()`. sdcHierarchies also contains a shiny-based app that can be called with `hier_app()` which allows to interactively change and modify a hierarchy and allows to convert tree- and data.frame based inputs into various formats. For more information, have a look at `?hier_app` and the other help-files of the package.

After all dimensions have been specified, these inputs must then be combined into a named list. In this object, the list-names refer to variable names of the input data set and the elements the data objects that hold the hierarchy specification itself. This is why the list elements of `dims` in this example have to be named `sex` and `age` as the specification refers to variables `age` and `sex` in input set `dat`.

    dims <- list(sex = dim_sex, age = dim_age)

### Setup a table instance

We now have prepared the inputs and can define a generic statistical table using `ck_setup()` as shown below:

    tab <- ck_setup(
      x = dat,
      rkey = "rkeys",
      dims = dims,
      w = "sampling_weight",
      countvars = "cnt_highincome",
      numvars = c("income", "savings"))

    ## computing contributing indices | rawdata <--> table; this might take a while

`ck_setup()` returns a `R6` class object that contains not only the relevant data but also all available methods. Thus, it is not required to assign the results of such methods to new objects, instead, the object itself is automatically updated.

These objects also have a custom print method, showing some general information about the object:

    print(tab)

    ## ── Table Information ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
    ## ✔ 21 cells in 2 dimensions ('sex', 'age')
    ## ✔ weights: yes
    ## ── Tabulated / Perturbed countvars ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
    ## ☐ 'total'
    ## ☐ 'cnt_highincome'
    ## ── Tabulated / Perturbed numvars ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
    ## ☐ 'income'
    ## ☐ 'savings'

### Defining perturbation parameters

#### Perturbation parameters for count variables

The next task is to define parameters that are used to perturb count variables which can be achieved with `ck_params_cnts`. This function requires as input the result of either `pt_create_pParams`, `pt_create_pTable` or `create_cnt_ptable` from the ptable package. Please refer also to the documentation of this package for information on the required parameters. In this example we are going to use - amongst others - exemplary ptables that can are provided by the `ptable`-pkg for demonstration purposes:

    # two different perturbation parameter sets from the ptable-pkg
    # an example ptable provided directly
    ptab1 <- ptable::pt_ex_cnts()

    # creating a ptable by specifying parameters
    para2 <- ptable::create_cnt_ptable(
      D = 8, V = 3, js = 2, pstay = 0.5,
      optim = 1, mono = TRUE)

We then need to create the required inputs for the cellKey package.

    p_cnts1 <- ck_params_cnts(ptab = ptab1)
    p_cnts2 <- ck_params_cnts(ptab = para2)

`ck_params_cnts()` returns objects that can be used as inputs in method `params_cnts_set()`. In argument `v` one may specify count variables for which the supplied perturbation parameters should be used. If `v` is not specified, the perturbation parameters are used for all count variables.

    # use `p_cnts1` for variable "total" (which always exists)
    tab$params_cnts_set(val = p_cnts1, v = "total")

    ## --> setting perturbation parameters for variable 'total'

    # use `p_cnts2` for "cnt_highincome"
    tab$params_cnts_set(val = p_cnts2, v = "cnt_highincome")

    ## --> setting perturbation parameters for variable 'cnt_highincome'

It is therefore entirely possible to use different parameter sets for different variables. Modifying perturbation parameters for some variables is easy, too. It is only required to apply the `params_cnts_set()`-method again which will replace any previously defined parameters.

#### Perturbation parameters for continuous variables

Setting and defining perturbation parameters for continuous variables works similarily. The required functions are `ck_params_num()` to create input objects that can be set with the `params_nums_set` method. Please note that it is possibly by specifying the `path` argument in both `ck_params_nums()` and `ck_params_cnts()` to save the parameters additionally as yaml-file. Using `ck_read_yaml()`, these files can later be imported again. This feature is useful for re-using parameter settings.

The underlying framework on how to perturb continuous tables differs from the proposed method from `ABS`. One possible approach is based on a “flex function”. This approach (which is described in deliverable `D4.2` in the project “perturbative confidentiality methods”) allows to apply different magnitude of noise to larger and smaller cells. Users can define the required parameters for the flex-approach with function `ck_flexparams()`. The required inputs are:

- `fp`: the flexpoint defining at which point should the underlying noise coefficient function reach its desired maximum (which is defined by the first element of `p`)
- `p`: numeric vector of length `2` with `p[1] > p[2]` where both elements specify a percentage. The first value refers to the desired maximum perturbation percentage for small cells (depending on fp) while the second element refers to the desired maximum perturbation percentage for large cells.
- `epsilon`: a numeric vector in descending order with all values in `[0; 1]` and with the first element forced to equal `1`. The length of this parameter must correspond with the number of `top_k` specified in `ck_params_nums()` (which will be discussed later).

    # parameters for the flex-function
    p_flex <- ck_flexparams(
      fp = 1000,
      p = c(0.3, 0.03),
      epsilon = c(1, 0.5, 0.2))

In the cellKey package it is possible to select the underlying data that form the base for the perturbation differently. In `ck_params_nums()` the specific approach can be selected in argument `type`. The valid choices for this argument are:

- `"top_contr"`: the `k` largest contributions to each cell are used in the perturbation procedure with the number `k` required to be specified in argument `top_k`
- `"mean"`: weighted cellmeans are used as starting points
- `"range"`: the difference between largest and smallest unweighted contributions for each cell are used as base for the perturbation procedure
- `"sum"`: weighted cellvalues are used as starting points for the perturbation

Another, more basic approach, is to use a constant perturbation magnitude for all cells, independent on their (weighted) values. The required parameters can be defined with `ck_simpleparams()` as shown below:

    # parameters for the simple approach
    p_simple <- ck_simpleparams(
      p = 0.05,
      epsilon = 1)

In this appraoch it is only required to specify a single percentage value `p` and - as in the case for the flex function - a vector of epsilons that are used in the case when `top_k > 1`.

Further important parameters for `ck_params_nums()` are:

- `mu_c`: an extra amount of perturbation applied to sensitive cells (restricted to the first of `top_k` noise components). In the following example we demonstrate how to identify sensitive cells for numeric variables.
- `same_key`: a logical value specifying if the original cell key (`TRUE`) should be used for the lookup of the largest contributor of a cell or if a perturbation of the cellkey itself (`FALSE`) should take place.
- `use_zero_rkeys`: a logical value defining if record keys of units not contributing to a specific numeric variables should be used (`TRUE`) or ignored (`FALSE`) when cell keys are computed.

A very important parameter is `ptab` which actually holds the perturbation tables in which perturbation values are looked up. This input can be specified differently in the case when numeric variables should be perturbed. It can be either an object derived from `ptable::pt_create_pTable(..., table = "nums")` in the most simple case. More advanced is to supply a named list, where the allowed names are shown below and each element must be the output of `ptable::pt_create_pTable(..., table = "nums")`.

- `"all"`: this ptable will be used for all cells; if specified, list-elements named `"even"`or `"odd"` are ignored
- `"even"`: this perturbation table will be used to look up perturbation values for cells with an even number of contributors
- `"odd"`: will be used to look up perturbation values for cells with an odd number of contributors
- `"small_cells"`: if specified, this ptable will be used to extract perturbation values for very small cells

Please note, that if the goal is to have different perturbation tables for cells with an even/odd number of contributors, both `"even"` or `"odd"` must be available in the input list. In the chunk below we create four different perturbation tables. For details on the parameters, please look at the documentation of the `ptable` package, especially in `ptable::create_num_ptable()`.

    # same ptable for all cells except for very small ones
    ex_ptab1 <- ptable::pt_ex_nums(parity = TRUE, separation = TRUE)

We can now use these tables to finally create objects containing all the required information to create perturbed magnitude tables using `ck_params_nums`. In the first case we want the same perturbation table (`ptab_all`) for cells with an even/odd number of contributors but want to use `ptab_sc` for very small cells.

    p_nums1 <- ck_params_nums(
      type = "top_contr",
      top_k = 3,
      ptab = ex_ptab1,
      mult_params = p_flex,
      mu_c = 2,
      same_key = FALSE,
      use_zero_rkeys = TRUE)

The second input we generate should use different ptables for cells with an even/odd number of contributing units (`ptab_even` and `ptab_odd`) but should not use a specific perturbation table for very small cells.

    ex_ptab2 <- ptable::pt_ex_nums(parity = FALSE, separation = FALSE)

As above, we need to use `ck_params_nums()` to compute suitable inputs.

    p_nums2 <- ck_params_nums(
      type = "mean",
      ptab = ex_ptab2,
      mult_params = p_simple,
      mu_c = 1.5,
      same_key = FALSE,
      use_zero_rkeys = TRUE)

The package internally computes the separation point that is used for very small cells in case this is required. Details on this can also be found in deliverable `D4.2` from the “perturbative confidentiality methods” project.

Now we can attach the results from `ck_params_nums()` to numeric variables using the `params_nums_set()`-method as shown below:

    tab$params_nums_set(v = "income", val = p_nums1)

    ## --> setting perturbation parameters for variable 'income'

    tab$params_nums_set(v = "savings", val = p_nums1)

    ## --> setting perturbation parameters for variable 'savings'

In order to make use of parameter `mu_c` that allows ab add extra amount of protection to sensitive cells, one may identify sensitive cells according to some rules. The following methods to identify sensitive cells are implemented:

- `supp_p()`: identify sensitive cells based on p%-rule
- `supp_pp()`: identify sensitive cells based on pq%-rule
- `supp_nk()`: identify sensitive cells based on nk-dominance rule
- `supp_freq()`: identify sensitive cells based on minimal frequencies for (weighted) number of contributors
- `supp_val()`: identify sensitive cells based on (weighted) cell values
- `supp_cells()`: identify sensitive cells based on their “names”

We now want to set all cells for variable `income` as sensitive to which less than `15` units contribute.

    tab$supp_freq(v = "income", n = 15, weighted = FALSE)

    ## freq-rule: 3 new sensitive cells (incl. duplicates) found (total: 3)

To set specific cells independent on values but their names, one may use the `$supp_cells()`-method. This cell requires a `data.frame` as input that contains a column for each dimensional variable specified. Each row of this input is considered as a cell where `NAs` are used as placeholders that match any characteristic of the relevant variable. Using the `data.frame` `inp` show below, the programm would suppress the following cells:

- `female` x `age_group1`
- `male` x `age_group3`
- `male` x any age group available in the data

    inp <- data.frame(
      "sex" = c("female", "male", "male"),
      "age" = c("age_group1", "age_group3", NA)
    )

### Compute perturbed tables

It is now possible to finally perturbed variables using the `perturb()`-method. As `tab` - the object created with `ck_setup()` - already contains all possible data, the only required input is the name of a variable that should be perturbed.

- `v`: a character vector specifying one or more variables that should be perturbed.

    tab$perturb(v = "total")

    ## Count variable 'total' was perturbed.

After this call, object `tab` is updated and contains now also perturbed values for variable `total`. We note that no explicit assignment is required. The following code shows that we also can perturb cnt- and numerical variables in one single call.

    tab$perturb(v = c("cnt_highincome", "savings", "income"))

    ## Count variable 'cnt_highincome' was perturbed.

    ## Numeric variable 'savings' was perturbed.

    ## Numeric variable 'income' was perturbed.

A `data.table` containing original and perturbed values can now be extracted using the `freqtab()`- and `numtab()` methods as discussed next.

### Extracting results

#### Obtain perturbed tables for count tables

Applying the `freqtab()`-method to one ore more already perturbed variables returns a `data.table` that contains for each table cell the unpertubed and perturbed (weighted and/or unweighted) counts. This function has the following arguments:

- `v`: one or more variable names of already perturbed count variables
- `path`: if not `NULL`, a (relative or absolute) path to which the resulting output table should be written. A `csv` file will be generated and `.csv` will be appended to the value provided.

The method returns a `data.table` with all combinations of the dimensional variables in the first \(n\) columns and after those the following columns:

- `vname`: name of the perturbed variable
- `uwc`: unweighted counts
- `wc`: weighted counts
- `puwc`: perturbed unweighted counts
- `pwc`: perturbed weighted counts

    tab$freqtab(v = c("total", "cnt_highincome"))

    ##        sex        age          vname  uwc     wc puwc         pwc
    ##  1:  Total      Total          total 4580 274539 4580 274539.0000
    ##  2:  Total age_group1          total 1969 118266 1971 118386.1280
    ##  3:  Total age_group2          total 1143  68240 1141  68120.5949
    ##  4:  Total age_group3          total  864  52423  865  52483.6748
    ##  5:  Total age_group4          total  423  25024  424  25083.1584
    ##  6:  Total age_group5          total  168   9783  167   9724.7679
    ##  7:  Total age_group6          total   13    803   14    864.7692
    ##  8:   male      Total          total 2296 138169 2298 138289.3563
    ##  9:   male age_group1          total 1015  61280 1014  61219.6256
    ## 10:   male age_group2          total  571  33840  572  33899.2644
    ## 11:   male age_group3          total  424  25818  424  25818.0000
    ## 12:   male age_group4          total  195  11762  193  11641.3641
    ## 13:   male age_group5          total   84   4969   83   4909.8452
    ## 14:   male age_group6          total    7    500    6    428.5714
    ## 15: female      Total          total 2284 136370 2285 136429.7067
    ## 16: female age_group1          total  954  56986  955  57045.7338
    ## 17: female age_group2          total  572  34400  571  34339.8601
    ## 18: female age_group3          total  440  26605  440  26605.0000
    ## 19: female age_group4          total  228  13262  229  13320.1667
    ## 20: female age_group5          total   84   4814   86   4928.6190
    ## 21: female age_group6          total    6    303    7    353.5000
    ## 22:  Total      Total cnt_highincome  445  27952  445  27952.0000
    ## 23:  Total age_group1 cnt_highincome  192  11711  192  11711.0000
    ## 24:  Total age_group2 cnt_highincome  123   7970  121   7840.4065
    ## 25:  Total age_group3 cnt_highincome   82   5241   84   5368.8293
    ## 26:  Total age_group4 cnt_highincome   34   2068   33   2007.1765
    ## 27:  Total age_group5 cnt_highincome   14    962   15   1030.7143
    ## 28:  Total age_group6 cnt_highincome    0      0    0      0.0000
    ## 29:   male      Total cnt_highincome  219  13740  219  13740.0000
    ## 30:   male age_group1 cnt_highincome   90   5554   89   5492.2889
    ## 31:   male age_group2 cnt_highincome   66   4249   67   4313.3788
    ## 32:   male age_group3 cnt_highincome   41   2596   38   2406.0488
    ## 33:   male age_group4 cnt_highincome   15    826   13    715.8667
    ## 34:   male age_group5 cnt_highincome    7    515    5    367.8571
    ## 35:   male age_group6 cnt_highincome    0      0    0      0.0000
    ## 36: female      Total cnt_highincome  226  14212  226  14212.0000
    ## 37: female age_group1 cnt_highincome  102   6157  102   6157.0000
    ## 38: female age_group2 cnt_highincome   57   3721   57   3721.0000
    ## 39: female age_group3 cnt_highincome   41   2645   43   2774.0244
    ## 40: female age_group4 cnt_highincome   19   1242   16   1045.8947
    ## 41: female age_group5 cnt_highincome    7    447    7    447.0000
    ## 42: female age_group6 cnt_highincome    0      0    0      0.0000
    ##        sex        age          vname  uwc     wc puwc         pwc

#### Obtain perturbed tables for magnitude tables

Using the `numtab()`-method allows to extract results for continuous variables. The required inputs are the same as for the `freqtab()`-method and the output returns a `data.table` with all combinations of the dimensional variables in the first \(n\) columns and the following additional columns:

- `vname`: name of the perturbed variable
- `uws`: unweighted sum
- `ws`: weighted cellsum
- `pws`: perturbed weighted sum of the given cell

We now have a look at the results of the variables `savings` and `income` that we already have perturbed.

    tab$numtab(v = c("savings", "income"))

    ##        sex        age   vname      uws         ws          pws
    ##  1:  Total      Total savings  2273532  136080418  136083334.6
    ##  2:  Total age_group1 savings   982386   59144910   59145634.6
    ##  3:  Total age_group2 savings   552336   33029243   33020004.3
    ##  4:  Total age_group3 savings   437101   26414476   26414183.8
    ##  5:  Total age_group4 savings   214661   12446392   12450800.0
    ##  6:  Total age_group5 savings    80451    4652696    4649450.9
    ##  7:  Total age_group6 savings     6597     392701     387830.2
    ##  8:   male      Total savings  1159816   69597198   69597973.2
    ##  9:   male age_group1 savings   517660   31321038   31319611.9
    ## 10:   male age_group2 savings   280923   16698089   16701276.7
    ## 11:   male age_group3 savings   214970   13022302   13020960.2
    ## 12:   male age_group4 savings    99420    5898725    5897903.7
    ## 13:   male age_group5 savings    43233    2424795    2422438.3
    ## 14:   male age_group6 savings     3610     232249     235965.8
    ## 15: female      Total savings  1113716   66483220   66480940.2
    ## 16: female age_group1 savings   464726   27823872   27824463.1
    ## 17: female age_group2 savings   271413   16331154   16329804.8
    ## 18: female age_group3 savings   222131   13392174   13384080.8
    ## 19: female age_group4 savings   115241    6547667    6543530.2
    ## 20: female age_group5 savings    37218    2227901    2228793.7
    ## 21: female age_group6 savings     2987     160452     159026.2
    ## 22:  Total      Total  income 22952978 1383502675 1383529374.5
    ## 23:  Total age_group1  income  9810547  592603578  592606204.7
    ## 24:  Total age_group2  income  5692119  342438723  342358132.6
    ## 25:  Total age_group3  income  4406946  266030114  266027152.7
    ## 26:  Total age_group4  income  2133543  128527130  128580753.7
    ## 27:  Total age_group5  income   848151   50260125   50213557.5
    ## 28:  Total age_group6  income    61672    3643005    3523883.0
    ## 29:   male      Total  income 11262049  678098383  678119173.7
    ## 30:   male age_group1  income  4877164  294298901  294309048.5
    ## 31:   male age_group2  income  2811379  167003473  167018345.8
    ## 32:   male age_group3  income  2168169  130191360  130158511.4
    ## 33:   male age_group4  income   978510   60622639   60618171.8
    ## 34:   male age_group5  income   393134   23699275   23669073.3
    ## 35:   male age_group6  income    33693    2282735    2389104.5
    ## 36: female      Total  income 11690929  705404292  705401451.7
    ## 37: female age_group1  income  4933383  298304677  298301046.4
    ## 38: female age_group2  income  2880740  175435250  175426717.7
    ## 39: female age_group3  income  2238777  135838754  135753178.2
    ## 40: female age_group4  income  1155033   67904491   67849918.6
    ## 41: female age_group5  income   455017   26560850   26560686.8
    ## 42: female age_group6  income    27979    1360270    1308671.0
    ##        sex        age   vname      uws         ws          pws

### Utility-measures

#### Utility measures for count variables

Method `measures_cnts()` allows to compute information loss measures for perturbed count variables. Its application is as simple as:

    tab$measures_cnts(v = "total", exclude_zeros = TRUE)

    ## $overview
    ##    noise cnt       pct
    ## 1:    -2   3 0.1428571
    ## 2:    -1   8 0.3809524
    ## 3:     0   3 0.1428571
    ## 4:     1   5 0.2380952
    ## 5:     2   2 0.0952381
    ##
    ## $measures
    ##       what    d1    d2    d3
    ##  1:    Min 0.000 0.000 0.000
    ##  2:    Q10 0.000 0.000 0.000
    ##  3:    Q20 1.000 0.001 0.016
    ##  4:    Q30 1.000 0.001 0.017
    ##  5:    Q40 1.000 0.001 0.021
    ##  6:   Mean 1.095 0.022 0.049
    ##  7: Median 1.000 0.002 0.023
    ##  8:    Q60 1.000 0.002 0.030
    ##  9:    Q70 1.000 0.006 0.039
    ## 10:    Q80 2.000 0.012 0.072
    ## 11:    Q90 2.000 0.077 0.136
    ## 12:    Q95 2.000 0.143 0.196
    ## 13:    Q99 2.000 0.162 0.196
    ## 14:    Max 2.000 0.167 0.196
    ##
    ## $cumdistr_d1
    ##    cat cnt       pct
    ## 1:   0   3 0.1428571
    ## 2:   1  16 0.7619048
    ## 3:   2  21 1.0000000
    ##
    ## $cumdistr_d2
    ##            cat cnt       pct
    ## 1:    [0,0.02]  17 0.8095238
    ## 2: (0.02,0.05]  18 0.8571429
    ## 3:  (0.05,0.1]  19 0.9047619
    ## 4:   (0.1,0.2]  21 1.0000000
    ## 5:   (0.2,0.3]  21 1.0000000
    ## 6:   (0.3,0.4]  21 1.0000000
    ## 7:   (0.4,0.5]  21 1.0000000
    ## 8:   (0.5,Inf]  21 1.0000000
    ##
    ## $cumdistr_d3
    ##            cat cnt       pct
    ## 1:    [0,0.02]   7 0.3333333
    ## 2: (0.02,0.05]  15 0.7142857
    ## 3:  (0.05,0.1]  17 0.8095238
    ## 4:   (0.1,0.2]  21 1.0000000
    ## 5:   (0.2,0.3]  21 1.0000000
    ## 6:   (0.3,0.4]  21 1.0000000
    ## 7:   (0.4,0.5]  21 1.0000000
    ## 8:   (0.5,Inf]  21 1.0000000
    ##
    ## $false_zero
    ## [1] 0
    ##
    ## $false_nonzero
    ## [1] 0
    ##
    ## $exclude_zeros
    ## [1] TRUE

This function returns a list with several utility measures that are now discussed. For further information have a look at `?ck_cnt_measures` as the same set of measures can also be computed for two vectors containing original and perturbed values.

- `overview`: a `data.table` with the following three columns:
  - noise: amount of noise computed as orig - pert
  - cnt: number of cells perturbed with the value given in column noise
  - pct: percentage of cells perturbed with the value given in column noise
- `measures`: a `data.table` containing measures of the distribution of three different distances between original and perturbed values of the unweighted counts. Column `what` specifies the computed measure. The three distances considered are:
  - `d1`: absolute distance between original and masked values
  - `d2`: relative absolute distance between original and masked values
  - `d3`: absolute distance between square-roots of original and perturbed values
- `cumdistr_d1`, `cumdistr_d2` and `cumdistr_d3`: for each distance d1, d2 and d3, a data.table with the following three columns:
  - `cat`: a specific value (for d1) or interval (for distances d2 and d3)
  - `cnt`: number of records smaller or equal the value in column cat for the given distance
  - `pct`: proportion of records smaller or equal the value in column cat for the selected distance
- `false_zero`: number of cells that were perturbed to zero
- `false_nonzero`: number of cells that were initially zero but have been perturbed to a number different from zero

If argument `exclude_zeros` is `TRUE` (the default setting), empty cells are excluded when computing distance-based measures `d1`, `d2` and `d3`.

#### Utility measures for continuous variables

For now, no utility measures for continuous variables are available. This will change in a future version.

### Additional Features

Finally we note that there are `print` and `summary` methods implemented for objects created with from `ck_setup()` which can be used as shown below:

The `print()`-method shows the dimension of the table as well as the variables that are (or possibly can be) perturbed. It is also displayed if the table was constructed using weights.

    tab$print() # same as (print(tab))

    ## ── Table Information ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
    ## ✔ 21 cells in 2 dimensions ('sex', 'age')
    ## ✔ weights: yes
    ## ── Tabulated / Perturbed countvars ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
    ## ☒ 'total' (perturbed)
    ## ☒ 'cnt_highincome' (perturbed)
    ## ── Tabulated / Perturbed numvars ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
    ## ☒ 'income' (perturbed)
    ## ☒ 'savings' (perturbed)

The `summary`-method shows some utility statistics for already perturbed variables as shown below:

    tab$summary()

    ## ┌──────────────────────────────────────────────┐
    ## │Utility measures for perturbed count variables│
    ## └──────────────────────────────────────────────┘
    ## ── Distribution statistics of perturbations ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
    ##          countvar Min Q10 Q20 Q30 Q40   Mean Median Q60 Q70 Q80 Q90 Q95 Q99 Max
    ## 1:          total  -2  -1  -1  -1   0  0.238      1   1   1   1   2   2   2   2
    ## 2: cnt_highincome  -3  -2  -2  -1   0 -0.381      0   0   0   0   1   2   2   2
    ##
    ## ── Distance-based measures ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
    ## ✔ Variable: 'total'
    ##
    ##       what    d1    d2    d3
    ##  1:    Min 0.000 0.000 0.000
    ##  2:    Q10 0.000 0.000 0.000
    ##  3:    Q20 1.000 0.001 0.016
    ##  4:    Q30 1.000 0.001 0.017
    ##  5:    Q40 1.000 0.001 0.021
    ##  6:   Mean 1.095 0.022 0.049
    ##  7: Median 1.000 0.002 0.023
    ##  8:    Q60 1.000 0.002 0.030
    ##  9:    Q70 1.000 0.006 0.039
    ## 10:    Q80 2.000 0.012 0.072
    ## 11:    Q90 2.000 0.077 0.136
    ## 12:    Q95 2.000 0.143 0.196
    ## 13:    Q99 2.000 0.162 0.196
    ## 14:    Max 2.000 0.167 0.196
    ##
    ## ✔ Variable: 'cnt_highincome'
    ##
    ##       what    d1    d2    d3
    ##  1:    Min 0.000 0.000 0.000
    ##  2:    Q10 0.000 0.000 0.000
    ##  3:    Q20 0.000 0.000 0.000
    ##  4:    Q30 0.000 0.000 0.000
    ##  5:    Q40 0.800 0.009 0.042
    ##  6:   Mean 1.111 0.048 0.109
    ##  7: Median 1.000 0.016 0.074
    ##  8:    Q60 1.200 0.025 0.094
    ##  9:    Q70 2.000 0.047 0.129
    ## 10:    Q80 2.000 0.072 0.205
    ## 11:    Q90 2.300 0.141 0.295
    ## 12:    Q95 3.000 0.177 0.367
    ## 13:    Q99 3.000 0.264 0.401
    ## 14:    Max 3.000 0.286 0.410
    ##
    ## ┌──────────────────────────────────────────────────┐
    ## │Utility measures for perturbed numerical variables│
    ## └──────────────────────────────────────────────────┘
    ## ── Distribution statistics of perturbations ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
    ##      vname         Min        Q10        Q20        Q30       Q40       Mean    Median       Q60      Q70      Q80       Q90       Q95       Q99        Max
    ## 1:  income -119121.989 -80590.410 -51599.027 -32848.599 -8532.253 -13740.085 -3630.625 -2840.283 2626.699 14872.81 26699.529 53623.684 95820.303 106369.457
    ## 2: savings   -9238.676  -4870.839  -3245.138  -2279.765 -1425.807  -1126.899 -1341.809  -292.242  724.624   892.72  3187.721  3716.843  4269.743   4407.969

## Summary

The package is “work in progress” and therefore, suggestions and/or bugreports are welcome. Please feel free to file an issue at our issue tracker or contribute to the package by filing a pull request against the master branch.