dUtility | R Documentation |
dUtility()
allows to compute different measures of data-utility based
on various distances using original and perturbed variables.
dUtility(obj, ...)
obj |
original data or object of class sdcMicroObj |
... |
see arguments below
|
The standardised distances of the perturbed data values to the original ones are measured. The following measures are available:
"IL1
: sum of absolute distances between original and perturbed variables
scaled by absolute values of the original variables
"IL1s
: measures the absolute distances between original
and perturbed ones, scaled by the standard deviation of original variables times
the square root of 2
.
"eigen
; compares the eigenvalues of original and perturbed data
"robeigen
; compares robust eigenvalues of original and perturbed data
data utility or modified entry for data utility the sdcMicroObj.
Matthias Templ
for IL1 and IL1s: see Mateo-Sanz, Sebe, Domingo-Ferrer. Outlier Protection in Continuous Microdata Masking. International Workshop on Privacy in Statistical Databases. PSD 2004: Privacy in Statistical Databases pp 201-215.
Templ, M. and Meindl, B., Robust Statistics Meets SDC: New Disclosure Risk Measures for Continuous Microdata Masking
, Lecture Notes in Computer
Science, Privacy in Statistical Databases, vol. 5262, pp. 113-126, 2008.
dRisk()
, dRiskRMD()
data(free1)
free1 <- as.data.frame(free1)
m1 <- microaggregation(free1[, 31:34], method="onedims", aggr=3)
m2 <- microaggregation(free1[, 31:34], method="pca", aggr=3)
dRisk(obj=free1[, 31:34], xm=m1$mx)
dRisk(obj=free1[, 31:34], xm=m2$mx)
dUtility(obj=free1[, 31:34], xm=m1$mx)
dUtility(obj=free1[, 31:34], xm=m2$mx)
data(Tarragona)
x <- Tarragona[, 5:7]
y <- addNoise(x)$xm
dRiskRMD(x, xm=y)
dRisk(x, xm=y)
dUtility(x, xm = y, method = "IL1")
dUtility(x, xm = y, method = "IL1s")
dUtility(x, xm = y, method = "eigen")
dUtility(x, xm = y, method = "robeigen")
## for objects of class sdcMicro:
data(testdata2)
sdc <- createSdcObj(testdata2,
keyVars=c('urbrur','roof','walls','water','electcon','relat','sex'),
numVars=c('expend','income','savings'), w='sampling_weight')
## this is already made internally:
## sdc <- dUtility(sdc)
## and already stored in sdc
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