`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`

.

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 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#> [1] Infm@gammaSensitivity ## NA as Laplace is naturally eps-DP#> [1] NAP <- function(n) rnorm(n) m <- sensitivitySampler(m, oracle = P, n = 100, gamma = 0.33)#>m@sensitivity ## small like 0.03...#> [1] 0.03416313m@gammaSensitivity ## 0.33 as directed, now m is (eps,gam)-DP.#> [1] 0.33