Masking, Unmasking and Restoring Confidential Data

Three key functionalities are present. It is able to mask confidential data using multiplicative noise. It is able to unmask this data while still preserving confidentiality. It is able to calculate the numerical joint density function of the original data from the unmasked data, as well as obtaining a sample from the marginal density functions of the unmasked data. The final results are a reasonable approximation to the original data for the purposes of analysis (Lin et al. (2018) < http://www.tdp.cat/issues16/abs.a271a17.php>).


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install.packages("MaskJointDensity")

1.0 by Luke Mazur, a year ago


Browse source code at https://github.com/cran/MaskJointDensity


Authors: Yan-Xia Lin [aut, cre] , Luke Mazur [aut, cre] , Jordan Morris [ctb]


Documentation:   PDF Manual  


GPL-2 license


Imports ks, np, plyr, parallel, MASS


See at CRAN