Laplace-Mechanism: Laplace Mechanism

In IQSS/PSI-Library: Differentially Private Statistical Releases for Privacy Preservation

Description

Differentially private evaluation of input function "fun" with sensitivity "sens" on input data "x" using the Laplace mechanism.

Arguments

fun	function of input x to add Laplace noise to.
x	input that function fun will be evaluated on.
sens	sensitivity of fun. Sensitivity is defined in above citation.
...	any additional (optional) parameters

Value

result of post-processing on input function "fun" evaluated on database "x", assuming sensitivity of fun is "sens".

References

C. Dwork, A. Roth The Algorithmic Foundations of Differential Privacy, Chapter 3.3 The Laplace Mechanism p.30-37. August 2014.

Examples

# histogram example

# the function in `statistic-histogram.R` that creates a histogram from input data
histogram_function <- fun.hist 
# the data frame that holds the data we want to analyze, in this case the data is called "PUMS5extract10000"
data <- data(PUMS5extract10000) 
# the variable for which we want a histogram
variable <- "age"
# the sensitivity for the histogram, the default sensitivity for histograms is 2 
sens <- 2 
# the post-processing function to use to format the histogram release correctly
post_processing_function <- dpHistogram$postProcess 

laplace_histogram <- mechanismLaplace$evaluate(histogram_function, data[, variable], sens, post_processing_function)

# mean example

mean_function <- mean
# the sensitivity for a differntially private mean in calculated as the difference in the data range divided by the number of data points
sens <- diff(rng) / n 
# the post-processing function to use to format the mean release correctly
post_processing_function <- dpMean$postProcess 
# `data` and `variable` same as above

laplace_mean <- mechanismLaplace$evaluate(mean_function, data[, variable], sens, post_processing_function)

IQSS/PSI-Library documentation built on Feb. 15, 2020, 9:03 p.m.