addNoise {sdcMicro}R Documentation

Adding noise for the perturbation of data

Description

Various adding noise methods for the perturbation of continuous scaled variables can be used.

Usage

addNoise(x, noise = 150, method = "additive", p = 0.001, delta=0.1)

Arguments

x data frame or matrix which should be perturbed
noise amount of noise (in percentages)
method choose between ‘additive’, ‘correlated’, ‘correlated2’, ‘restr’, ‘ROMM’, ‘outdect’
p multiplication factor for method ‘ROMM’
delta parameter for method ‘correlated2’, details can be found in the reference below.

Details

Method ‘additive’ adds noise completely at random to each variable depending on there size and standard deviation. ‘correlated’ and method ‘correlated2’ adds noise and preserves the covariances as descriped in R. Brand (2001) or in the reference given below. Method ‘restr’ takes the sample size into account when adding noise. Method ‘ROMM’ is an implementation of the algorithm ROMM (Random Orthogonalized Matrix Masking) (Fienberg, 2004). Method ‘outdect’ adds noise only to outliers. The outliers are idedentified with univariate and robust multivariate procedures based on a robust mahalanobis distancs calculated by the MCD estimator.

Value

An object of class “micro” with following entities:

x the original data
xm the modified (perturbed) data
method method used for perturbation
noise amount of noise

Author(s)

Matthias Templ

References

Domingo-Ferrer, J. and Sebe, F. and Castella, J., “On the security of noise aliition for privacy in statistical databases”, Lecture Notes in Computer Science, vol. 3050, pp. 149-161, 2004. ISSN 0302-9743. Vol. Privacy in Statistical Databases, eds. J. Domingo-Ferrer and V. Torra, Berlin: Springer-Verlag. http://vneumann.etse.urv.es/publications/sci/lncs3050OntheSec.pdf,

Ting and Fienberg, “ROMM Methodology for Microdata Release” Joint UNECE/Eurostat work session on statistical data confidentiality, Geneva, Switzerland, 2005, http://www.niss.org/dgii/TR/wp.11.e(ROMM).pdf

Templ, M. and Meindl, B., Robustification of Microdata Masking Methods and the Comparison with Existing Methods, Lecture Notes in Computer Science, Privacy in Statistical Databases, vol. 5262, pp. 177-189, 2008.

Templ, M. New Developments in Statistical Disclosure Control and Imputation: Robust Statistics Applied to Official Statistics, Suedwestdeutscher Verlag fuer Hochschulschriften, 2009, ISBN: 3838108280, 264 pages.

See Also

summary.micro

Examples

data(Tarragona)
a1 <- addNoise(Tarragona)
a1
valTable(Tarragona[100:200,], method=c("addNoise: additive", "addNoise: correlated2"))

[Package sdcMicro version 2.6.4 Index]