Description Usage Arguments Value References Examples

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`

.

1 | ```
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 Ald<c3><a0>. "Pain-Free Random Differential Privacy with Sensitivity Sampling", accepted into the 34th International Conference on Machine Learning (ICML'2017), May 2017.

1 2 3 4 5 6 7 8 9 | ```
## 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.
``` |

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