MSEanalytic: Determine the expected MSE of a simple DPCDF from its...

In CDF.PSIdekick: Evaluate Differentially Private Algorithms for Publishing Cumulative Distribution Functions

Description

Generates the analytically expected Mean Squared Error of a dpCDF. introduced by random noise, SUPPOSING that the DP-CDF is through the use of a noisy binary tree.

Usage

MSEanalytic(eps, range, gran, data, ...)

Arguments

eps	Epsilon value for differential privacy control
range	A vector length 2 containing user-specified min and max to truncate the universe to
gran	The smallest unit of measurement in the data (one [year] for a list of ages)
data	The vector of data from which the DP CDF was/is computed
...	Optionally add additional parameters. This is primarily used to allow automated execution of varied diagnostic functions.

Value

The MSE guaranteed by the given parameter combination assuming it's built from the min and max inward from a DP-Histogram, with 95

Examples

MSEanalytic(.01, c(1,10),1, rexp(10000,.4))

CDF.PSIdekick documentation built on May 30, 2017, 5:09 a.m.