functionSUB: Build dpCDFs through use of a noisy tree with bin merging.

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

Description

The function first creates a k-ary aggregate tree on the histogram bins. It then utilizes epsilon1 in order to privately discover the best way to prune sub-trees in order to reduce the L2 error due to the noise addition. It then prunes the sub-trees of the original tree, and outputs it by utilizing epsilon2. Finally, it utilizes this output to compute and release the private CDF.

Usage

functionSUB(eps, cdfstep, data, range, gran, K = 2, ...)

Arguments

eps	Epsilon value for Differential privacy control
cdfstep	The step sized used in outputting the approximate CDF; the values output are [min, min + cdfstep], [min, min + 2 * cdfstep], etc.
data	A vector of the data (single variable to compute CDFs from)
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)
K	This sets the degree of the underlying tree.
...	Optionally add additional parameters.

Value

A list with 2 vectors: one is the y coordinates of the DP-CDF, the other is the abs values of the anlytically expected bounds for a similarly-constructed DP-CDF, at 95 percent probability made without merging.

Examples

functionSUB(eps = .01, cdfstep = .1, data = rexp(10000,.4), range= c(1,10), gran = .1, K= 2)

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