valTable: Comparison of different microaggregation methods

In sdcMicro: Statistical Disclosure Control Methods for Anonymization of Data and Risk Estimation

Comparison of different microaggregation methods

Description

A Function for the comparison of different perturbation methods.

Usage

valTable(
  x,
  method = c("simple", "onedims", "clustpppca", "addNoise: additive", "swappNum"),
  measure = "mean",
  clustermethod = "clara",
  aggr = 3,
  nc = 8,
  transf = "log",
  p = 15,
  noise = 15,
  w = 1:dim(x)[2],
  delta = 0.1
)

Arguments

x	a data.frame or a matrix
method	character vector defining names of microaggregation-, adding-noise or rank swapping methods.
measure	FUN for aggregation. Possible values are mean (default), median, trim, onestep.
clustermethod	clustermethod, if a method will need a clustering procedure
aggr	aggregation level (default=3)
nc	number of clusters. Necessary, if a method will need a clustering procedure
transf	Transformation of variables before clustering.
p	Swapping range, if method swappNum has been chosen
noise	noise addition, if an addNoise method has been chosen
w	variables for swapping, if method swappNum has been chosen
delta	parameter for adding noise method "correlated2"

Details

Tabularize the output from summary.micro(). Will be enhanced to all perturbation methods in future versions.

Methods for adding noise should be named via addNoise:{method}, e.g. addNoise:correlated, where {method} specifies the desired method as described in addNoise().

Value

Measures of information loss splitted for the comparison of different methods.

Author(s)

Matthias Templ

References

Templ, M. and Meindl, B., ⁠Software Development for SDC in R⁠, Lecture Notes in Computer Science, Privacy in Statistical Databases, vol. 4302, pp. 347-359, 2006.

See Also

microaggregation(), summary.micro()

Examples

data(Tarragona)

valTable(
  x = Tarragona[100:200, ],
  method=c("simple", "onedims", "pca")
)

sdcMicro documentation built on Sept. 27, 2023, 5:07 p.m.