sdcMicro (version 2.6.3)

localSupp2Wrapper: Local Suppression 2

Description

A wrapper function for function localSupp2 in order to guarantee k-anonymity.

Usage

localSupp2Wrapper(x, keyVars, w, importance=rep(1, length(keyVars)), method="minimizeSupp", kAnon=2)

Arguments

x

data frame or matrix

keyVars

column index of key variables

w

column index of sampling weights

importance

weights for each key variable, see localSupp2()

method

minimizeSupp (default), further methods will be included in future versions of the package

kAnon

parameter for k-anonymity.

Value

• Object from class localSupp2.
• xAnon
  resulting data with suppressions
• supps
  number of suppressions in the key variables
• totalSupps
  total number of suppressions.
• anonymity
  TRUE, if k-anonymity is achieved
• keyVars
  index of the key variables.
• importance
  weight vector for key variables
• kAnon
  k for k-anoymity

Details

This wrapper function guarantees k-anonymity. If function localSupp2() cannot be reach k-anonymity, localSupp2 must be re-run on the previous results as long as k-anoymity is reached. If k-anoymity cannot be achieved (because the entries of parameter importance includes too much zeros) the function breaks after a sub-optimal solution is obtained.

References

Templ, M. Statistical Disclosure Control for Microdata Using the R-Package sdcMicro, Transactions on Data Privacy, vol. 1, number 2, pp. 67-85, 2008. http://www.tdp.cat/issues/abs.a004a08.php

See Also

freqCalc, localSupp

Examples

## example from Capobianchi, Polettini and Lucarelli:
## same results as localSupp2
data(francdat)
localSupp2Wrapper(francdat, keyVars=c(2,4,5,6), w=8)
localSupp2Wrapper(francdat, keyVars=c(2,4,5,6), w=8, k=2)
localSupp2Wrapper(francdat, keyVars=c(2,4,5,6), w=8, k=4)

## we want to avoid missings in column 5:
l1 <- localSupp2Wrapper(francdat, keyVars=c(2,4,5,6), importance=c(1,1,0,1), w=8, kAnon=1)
l1$x
## we want to avoid missings in column 5 and allow missings in 1 only if
## is really necessary:
l1 <- localSupp2Wrapper(francdat, keyVars=c(2,4,5,6), importance=c(0.1,1,0,1), w=8, kAnon=1)
l1$x

## long computation time, wait some seconds to get an information
## about the estimated computing time.
## l = localSupp2(free1, keyVars=1:3, w=30, k=2, importance=c(0.1,1,0.8))