Construct primary suppressed difference matrix — KDisclosurePrimary • GaussSuppression

Function for constructing model matrix columns representing primary suppressed difference cells

Usage

KDisclosurePrimary(
  data,
  x,
  crossTable,
  freqVar,
  mc_hierarchies = NULL,
  coalition = 1,
  upper_bound = Inf,
  targeting = default_targeting,
  print_frames = FALSE,
  ...
)

Arguments

data: a data.frame representing the data set
x: ModelMatrix generated by parent function
crossTable: crossTable generated by parent function
freqVar: name of the frequency variable in data
mc_hierarchies: a hierarchy representing meaningful combinations to be protected. Default value is NULL.
coalition: numeric vector of length one, representing possible size of an attacking coalition. This parameter corresponds to the parameter k in the definition of k-disclosure.
upper_bound: Numeric value specifying the maximum cell frequency for which disclosure of belonging to the cell may be regarded as unacceptable. When freq > upper_bound, disclosure of belonging to the cell is regarded as acceptable regardless of the specification of the sensitive parameter. Default is Inf. Note that this parameter may also be useful for reducing computational burden.
targeting: NULL, a list, or a function that returns a list specifying attribute disclosure scenarios. See Details. Default is default_targeting.
print_frames: Logical or character. If TRUE, additional data frames are printed to the console. When mc_hierarchies is used, this includes a data frame with hidden results. In addition, a data frame containing the primary suppressed difference cells is printed. If set to "primary_cells", only the primary suppressed difference cells are printed. The default is FALSE.
...: parameters passed to children functions

Value

dgCMatrix corresponding to primary suppressed cells

Details

The targeting specification is a named list that may contain the following optional elements. References to crossTable below refer to a data frame that may be extended after applying mc_hierarchies.

identifying: A data frame containing selected rows from crossTable. Membership in the cells represented by these rows is regarded as information that an intruder may already know. If omitted, it defaults to crossTable.
sensitive: A data frame containing selected rows from crossTable. If an intruder can infer membership in the cells represented by these rows, this is considered an unacceptable disclosure, subject to any further specification provided by is_sensitive. If omitted, it defaults to crossTable.
is_sensitive: A data frame with the same structure as sensitive, but with logical variables. It indicates which codes in sensitive are regarded as sensitive. When specified, disclosure is assessed by which codes within a revealed cell are disclosed. If omitted, it is equivalent to a data frame where all elements are TRUE.
exclude_relations: A specification defining identifying–sensitive relations that are ignored. This may be given either as a sparse logical matrix (or a dummy matrix with values 0/1), or as a list of lists. In the matrix form, rows correspond to rows in sensitive (or crossTable if sensitive is not specified), and columns correspond to rows in identifying (or crossTable if identifying is not specified). In the list form, each list element specifies a set of relations by selecting rows from identifying and/or sensitive defined above. Each element may contain the components identifying and sensitive; omitted components default to all rows of the corresponding element. The full list jointly defines the relations to be excluded.
include_relations: As for exclude_relations, but defining the identifying–sensitive relations that are considered. Only the relations specified are included; all others are ignored.

Author

Daniel P. Lupp and Øyvind Langsrud