addNoise {sdcMicro} | R Documentation |
Various adding noise methods for the perturbation of continuous scaled variables can be used.
addNoise(x, noise = 150, method = "additive", p = 0.001, delta=0.1)
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. |
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.
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 |
Matthias Templ
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.
data(Tarragona) a1 <- addNoise(Tarragona) a1 valTable(Tarragona[100:200,], method=c("addNoise: additive", "addNoise: correlated2"))