sensitivitySampler,DPMech,function,numeric-method {diffpriv} | R Documentation |
DPMech-class
's.Given a constructed DPMech-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
.
## S4 method for signature 'DPMech,'function',numeric'
sensitivitySampler(object, oracle, n,
m = NA_integer_, gamma = NA_real_)
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. |
gamma |
RDP privacy confidence level. |
object
with updated gammaSensitivity
slot.
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.
## Simple example with unbounded data hence no global sensitivity.
f <- function(xs) mean(xs)
m <- DPMechLaplace(target = f, dims = 1)
m@sensitivity ## Inf
m@gammaSensitivity ## NA as Laplace is naturally eps-DP
P <- function(n) rnorm(n)
m <- sensitivitySampler(m, oracle = P, n = 100, gamma = 0.33)
m@sensitivity ## small like 0.03...
m@gammaSensitivity ## 0.33 as directed, now m is (eps,gam)-DP.