sensitivitySamplerManual,DPMechNumeric,function,numeric,numeric,numeric-method {diffpriv} | R Documentation |
DPMechNumeric-class
.Given a constructed DPMechNumeric-class
, complete with
target
function and sensitivityNorm,
and an oracle
for
producing records, samples the sensitivity of the target function to set the
mechanism's sensitivity
. Typically the method
sensitivitySampler
should be used instead; NOTE this method
does not properly set the gammaSensitivity
slot of
DPMech-class
unlike the preferred method. This method can
probe target
to determine response dimension when the
corresponding object@dims
is NA
.
## S4 method for signature 'DPMechNumeric,'function',numeric,numeric,numeric'
sensitivitySamplerManual(object,
oracle, n, m, k)
object |
an object of class |
oracle |
a source of random databases. A function returning: list,
matrix/data.frame (data in rows), numeric/character vector of records if
given desired length > 1; or single record given length 1, respectively
a list element, a row/named row, a single numeric/character. Whichever
type is used should be expected by |
n |
database size scalar positive numeric, integer-valued. |
m |
sensitivity sample size scalar positive numeric, integer-valued. |
k |
order statistic index in 1,..., |
object
with updated sensitivity parameter, and (potentially)
dims
.
Benjamin I. P. Rubinstein and Francesco AldÃ . "Pain-Free Random Differential Privacy with Sensitivity Sampling", accepted into the 34th International Conference on Machine Learning (ICML'2017), May 2017.
sensitivitySampler
preferred method for sensitivity
sampling.
## Simple example with unbounded data hence no global sensitivity.
f <- function(xs) mean(xs)
m <- DPMechLaplace(target = f, dims = 1)
P <- function(n) rnorm(n)
m <- sensitivitySamplerManual(m, oracle = P, n = 100, m = 10, k = 10)
m@sensitivity