addNoise: Adding noise for the perturbation of data

Description

Various adding noise methods for the perturbation of data can be used.

Usage

addNoise(x, noise = 15, method = "additive", p = 0.001, delta=0.1)

Arguments

data frame or matrix which should be perturbed

noise

amount of noise

method

choose between additive, correlated, correlated2, restr, ROMM, outdect

multiplication factor for method ROMM

delta

parmeter for method correlated2, see in the reference below

Value

An object of class micro with following entities:
xthe original data
xmthe modified (perturbed) data
methodmethod used for perturbation
noiseamount of noise

Details

Method additive adds noise completely random to each variable depending on there size and standard error. correlated and method correlated2 adds noise and preserves the covariances as descriped in R. Brand (2001) or in the reference given below. restr takes the sample size into account when adding noise. Method ROMM should be the implementation of the first algorithm of Fienberg (2004). Method outdect adds noise only to outliers. This outliers are idedentified with univariate and robust multivariate procedures based on a robust mahalanobis distancs calculated with the MCD estimator.

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

Examples

Run this code

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

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