Density approximant based on noise multiplied data: MaskDensity10.R and its applications

A framework, the sample-moment-based density approximant , for estimating the probability density function based on noise multiplied data was proposed in Lin (2014). Based on the framework, an R package, MaskDensity10.R, is built in this paper. The package is available from http://www.uow.edu.au/~yanxia/Confidential_data_analysis/. The framework is developed for continuous univeriates (see Lin, 2014). With the techniques of nonparametric smoothing and K-means clustering integrated, MaskDensity10.R can be used for estimating the mass functions of categorical variables. The same R package, MaskDensity10.R, can be used by the data agency to create the masked data set, as well as used by the end-user to obtain the approximation of the density function of the original data set based on masked data. Simulation studies and real life data applications of MaskDensity10.R are presented in this paper. The risk of disclosure in the application of the R package to microdata is discussed, particularly for category data.