unmaskAndGetSampleBatch: Batch function that allows the user to unmask a number of...
In MaskJointDensity: Masking, Unmasking and Restoring Confidential Data

Description Usage Arguments Details Value Author(s) References Examples

Use the sample-moment-based density approximant method to estimate the density function of univariate distributions based noise multiplied data. Afterwards if the Data Provider supplies all the means, standard deviations and the correlation matrix of the original data then these can be used as arguments. Otherwise, these are calculated using the mean of noise sample and the mean of the vector created by squaring each element of the noise sample.

A sample of the chosen size is then simulated from the estimated joint density function.

unmaskAndGetSampleBatch(listOfMaskedVectorsToBeUnmasked,
                                    listOfNoisefiles,
                                    mu, s, rho_X,
                                    cores = 1, size,
                                    verbose = -1,
                                    onlyUnmasked = FALSE)

`listOfMaskedVectorsToBeUnmasked`	list of masked vectors. The masked data were generated by R Function mask, or maskBatch.
`listOfNoisefiles`	Noise files containing a sample of the noise used to mask the vectors in listOfMaskedVectorsToBeUnmasked from R function mask, or maskBatch
`mu`	List of means of unmasked vectors - if not supplied will be estimated
`s`	List of standard deviations of unmasked vectors - if not supplied will be estimated
`rho_X`	Correlation matrix of unmasked vectors - if not supplied will be estimated
`cores`	Passed to mclapply
`size`	Passed to actualPosition
`verbose`	If greater than 0 output is printed to tell the user at what stage the function is in, is also passed to many internal functions and will give more detailed output from them if it is greater than 1
`onlyUnmasked`	If true then only the output from unmask is returned. Effectively this makes the function "unmaskBatch" - so to speak. False by default.

unmask is fully described in Lin and Fielding (2015). The theory used to support unmask can be found in Lin (2014). unmask implements the sample-moment-based density approximate method the estimated the smoothed density function of the original data based on their make data maskedVectorToBeUnmasked. The output of the function unmask is a set of sample data from the estimated mouthed density function.

Using this function is the equivalent of calling unmask once for each variable and then calling getSampleBasedOnUnmaskedData

If onlyUnmasked is false then returns a list with two elements, both of which are lists.

`unmaskedOutputs`	list containing the three (four if categorical) outputs from the function unmask namely: unmaskedVariable, meanOfNoise, meanOfSquaredNoise, prob
`getSampleOutputs`	list containing the output from getSampleBasedOnUnmaskedData which is a list containing vectors corresponding to samples for each variable

If onlyUnmasked is true then returns a list with only one element:

unmaskedOutputs

list containing the three (four if categorical) outputs from the function unmask namely: unmaskedVariable, meanOfNoise, meanOfSquaredNoise, prob

Luke Mazur

Lin, Yan-Xia (2014). Density approximant based on noise multiplied data. In J. Domingo-Ferrer (Eds.), Privacy in Statistical Databases 2014, LNCS 8744, Springer International Publishing Switzerland, 2014, pp. 89-104. Lin, Yan-Xia and Fielding, Mark James (2015). MaskDensity14: An R Package for the Density Approximant of a Univariate Based on Noise Multiplied Data, SoftwareX 34, 3743, doi:10.1016/j.softx.2015.11.002

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

##outputNL1 <- unmaskAndGetSampleBatch(listOfMaskedVectorsToBeUnmasked = ##list(ystar1, ystar2),
##                                    listOfNoisefiles = 
## (file.path(tempdir(),"noise1.bin"),file.path(tempdir(),"noise2.bin")),
##                                    cores = 1, size = 1000,
##                                    verbose = 2)
## not a real example because ultimately in order to demonstrate this
## function the entire package functionality must be demonstrated
## this is demonstrated in the package example