overal disclosure risk

Distance-based disclosure risk estimation via standard deviation-based intervals around observations.

dRisk(obj, ...)

Arguments

obj

a data.frame or object of class sdcMicroObj-class

...

possible arguments are:

xm:

perturbed data

k:

percentage of the standard deviation

Value

The disclosure risk or/and the modified sdcMicroObj-class

Details

An interval (based on the standard deviation) is built around each value of the perturbed value. Then we look if the original values lay in these intervals or not. With parameter k one can enlarge or down scale the interval.

References

see method SDID in 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. Statistical Disclosure Control for Microdata: Methods and Applications in R. Springer International Publishing, 287 pages, 2017. ISBN 978-3-319-50272-4. doi:10.1007/978-3-319-50272-4

See also

dUtility

Author

Matthias Templ

Examples

data(free1)
free1 <- as.data.frame(free1)
# \donttest{
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$mx)
#> [1] 0.9955
dRisk(obj=free1[, 31:34], xm=m2$mx)
#> [1] 0
dUtility(obj=free1[, 31:34], xm=m1$mx)
#> [1] 7.971673
dUtility(obj=free1[, 31:34], xm=m2$mx)
#> [1] 6335.63

## 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 <- dRisk(sdc)
## and already stored in sdc
# }