primarySuppression {sdcTable}

Apply primary suppression

Description

Function primarySuppression() is used to identify and suppress primary sensitive table cells in sdcProblem objects. Argument type allows to select a rule that should be used to identify primary sensitive cells. At the moment it is possible to identify and suppress sensitive table cells using the frequency-rule, the nk-dominance rule and the p-percent rule.

Usage

primarySuppression(object, type, ...)

Arguments

object

a sdcProblem object

type

character vector of length 1 defining the primary suppression rule. Allowed types are:

• freq: apply frequency rule with parameters maxN and allowZeros

• nk: apply nk-dominance rule with parameters n, k

• p: apply p-percent rule with parameter p

• pq: apply pq-rule with parameters p and q

...

parameters used in the identification of primary sensitive cells. Parameters that can be modified|changed are:

• maxN: numeric vector of length 1 used when applying the frequency rule. All cells having counts <= maxN are set as primary suppressed. The default value of maxN is 3.

• allowZeros: logical value defining if empty cells (with frequency = 0) should be considered sensitive when using the frequency rule. Empty cells are never considered as sensitive when applying dominance rules; The default value of allowZeros is FALSE so that empty cells are not considered primary sensitive by default. Such cells (frequency 0) are then flagged as z which indicates such a cell may be published but should (internally) not be used for (secondary) suppression in the heuristic algorithms.

• p: numeric vector of length 1 specifying parameter p that is used when applying the p-percent rule with default value of 80.

• pq: numeric vector of length 2 specifying parameters p and q that are used when applying the pq-rule with the default being c(25, 50).

• n: numeric vector of length 1 specifying parameter n that is used when applying the nk-dominance rule. Parameter n is set to 2 by default.

• k: scalar numeric specifying parameter k that is used when applying the nk-dominance rule. Parameter n is set to 85 by default.

• numVarName: character scalar specifying the name of the numerical variable that should be used to identify cells that are dominated by dominance rules (p-rule, pq-rule or nk-rule). This setting is mandatory in package versions ⁠>= 0.29⁠ If type is either 'nk', 'p' or 'pq', it is mandatory to specify either numVarInd or numVarName.

• numVarInd: same as numVarName but a scalar numeric specifying the index of the variable is expected. If both numVarName and numVarInd are specified, numVarName is used. The index refers to the index of the specified numvars in makeProblem(). This argument is no longer respected in versions ⁠>= 0.29⁠ where numVarName must be used.

Details

since versions ⁠>= 0.29⁠ it is no longer possible to specify underlying variables for dominance rules ("p", "pq" or "nk") by index; these variables must be set by name using argument numVarName.

Value

a sdcProblem object

Note

the nk-dominance rule, the p-percent rule and the pq-rule can only be applied if micro data have been used as input data to function makeProblem()

Author(s)

Bernhard Meindl [e-mail]

Examples

# load micro data
utils::data("microdata1", package = "sdcTable")

# load problem (as it was created in the example in ?makeProblem
p <- sdc_testproblem(with_supps = FALSE)

# we have a look at the frequency table by gender and region
xtabs(rep(1, nrow(microdata1)) ~ gender + region, data = microdata1)

# 2 units contribute to cell with region=='A' and gender=='female'
# --> this cell is considered sensitive according the the
# freq-rule with 'maxN' equal to 2!
p1 <- primarySuppression(
  object = p,
  type = "freq",
  maxN = 2
)

# we can also apply a p-percent rule with parameter "p" being 30 as below.
# This is only possible if we are dealing with micro data and we also
# have to specify the name of a numeric variable.
p2 <- primarySuppression(
  object = p,
  type = "p",
  p = 30,
  numVarName = "val"
)

# looking at anonymization states we see, that one cell is primary
# suppressed (sdcStatus == "u")
# the remaining cells are possible candidates for secondary cell
# suppression (sdcStatus == "s") given the frequency rule with
# parameter "maxN = 2".
#
# Applying the p-percent rule with parameter 'p = 30' resulted in
# two primary suppressions.
data.frame(
  p1_sdc = getInfo(p1, type = "sdcStatus"),
  p2_sdc = getInfo(p2, type = "sdcStatus")
)