Given a constructed
DPMech-class, complete with
sensitivityNorm, and an
oracle for producing
records, samples the sensitivity of the target function to set the
sensitivitySampler(object, oracle, n, m = NA_integer_, gamma = NA_real_)
an object of class
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
database size scalar positive numeric, integer-valued.
sensitivity sample size scalar positive numeric, integer-valued.
RDP privacy confidence level.
object with updated
Benjamin I. P. Rubinstein and Francesco Aldà. "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#>  Infm@gammaSensitivity ## NA as Laplace is naturally eps-DP#>  NAP <- function(n) rnorm(n) m <- sensitivitySampler(m, oracle = P, n = 100, gamma = 0.33)#>m@sensitivity ## small like 0.03...#>  0.03416313m@gammaSensitivity ## 0.33 as directed, now m is (eps,gam)-DP.#>  0.33