LocalRecProg function

Local recoding via Edmond's maximum weighted matching algorithm

To be used on both categorical and numeric input variables, although usage on categorical variables is the focus of the development of this software. methods

LocalRecProg(
  obj,
  ancestors = NULL,
  ancestor_setting = NULL,
  k_level = 2,
  FindLowestK = TRUE,
  weight = NULL,
  lowMemory = FALSE,
  missingValue = NA,
  ...
)

Arguments

• obj: a data.frame or a sdcMicroObj-class-object

• ancestors: Names of ancestors of the cateorical variables

• ancestor_setting: For each ancestor the corresponding categorical variable

• k_level: Level for k-anonymity

• FindLowestK: requests the program to look for the smallest k that results in complete matches of the data.

• weight: A weight for each variable (Default=1)

• lowMemory: Slower algorithm with less memory consumption

• missingValue: The output value for a suppressed value.

• ...: see arguments below

  • categorical: Names of categorical variables
  • numerical: Names of numerical variables

Returns

dataframe with original variables and the supressed variables (suffix _lr). / the modified sdcMicroObj-class

Details

Each record in the data represents a category of the original data, and hence all records in the input data should be unique by the N Input Variables. To achieve bigger category sizes (k-anoymity), one can form new categories based on the recoding result and repeatedly apply this algorithm.

Methods

• list("signature(obj="sdcMicroObj")"):

Examples

data(testdata2)
cat_vars <- c("urbrur", "roof", "walls", "water", "sex", "relat")
anc_var <- c("water2", "water3", "relat2")
anc_setting <- c("water","water","relat")

r1 <- LocalRecProg(
  obj = testdata2,
  categorical = cat_vars,
  missingValue = -99)
r2 <- LocalRecProg(
  obj = testdata2,
  categorical = cat_vars,
  ancestor = anc_var,
  ancestor_setting = anc_setting,
  missingValue = -99)
r3 <- LocalRecProg(
  obj = testdata2,
  categorical = cat_vars,
  ancestor = anc_var,
  ancestor_setting = anc_setting,
  missingValue = -99,
  FindLowestK = FALSE)

# for objects of class sdcMicro:
sdc <- createSdcObj(
  dat = testdata2,
  keyVars = c("urbrur", "roof", "walls", "water", "electcon", "relat", "sex"),
  numVars = c("expend", "income", "savings"),
  w = "sampling_weight")
sdc <- LocalRecProg(sdc)

References

Kowarik, A. and Templ, M. and Meindl, B. and Fonteneau, F. and Prantner, B.: Testing of IHSN Cpp Code and Inclusion of New Methods into sdcMicro, in: Lecture Notes in Computer Science, J. Domingo-Ferrer, I. Tinnirello (editors.); Springer, Berlin, 2012, ISBN: 978-3-642-33626-3, pp. 63-77. tools:::Rd_expr_doi("10.1007/978-3-642-33627-0_6")

Author(s)

Alexander Kowarik, Bernd Prantner, IHSN C++ source, Akimichi Takemura