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) <>).


Reference manual

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1.0 by Luke Mazur, 3 years ago

Browse source code at

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