DPMechBernstein-class: An S4 class for the Bernstein mechanism of differential...
In diffpriv: Easy Differential Privacy

Description Usage Arguments Value Methods (by generic) Slots References Examples

A class that implements the Bernstein mechanism (not iterated version) of differential privacy, for privatizing release of real-valued functions on [0,1]^l based on arbitrary datasets. Approximates the target on a lattice.

## S4 method for signature 'DPMechBernstein'
show(object)

## S4 method for signature 'DPMechBernstein,DPParamsEps'
releaseResponse(mechanism,
  privacyParams, X)

## S4 method for signature 'DPMechBernstein'
sensitivityNorm(mechanism, X1, X2)

`object`	an instance of class `DPMech`.
`mechanism`	an object of class `DPMechBernstein`.
`privacyParams`	an object of class `DPParamsEps`.
`X`	a privacy-sensitive dataset, if using sensitivity sampler a: list, matrix, data frame, numeric/character vector.
`X1`	a privacy-sensitive dataset.
`X2`	a privacy-sensitive dataset.

list with slots per argument, actual privacy parameter and response: mechanism response with length of target release: privacyParams, sensitivity, latticeK, dims, target, response.

scalar numeric norm of non-private target on datasets. The L_∞ of the functions on a lattice.

show: automatically prints the object.
releaseResponse: releases Bernstein mechanism responses.
sensitivityNorm: measures target sensitivity.

sensitivity: non-negative scalar numeric maximum absolute target sensitivity maximized over the lattice. Defaults to Inf for use with sensitivitySampler().
target: might be a closure that takes arbitrary dataset and returns a real-valued function on [0,1]^l.
gammaSensitivity: NA_real_ if inactive, or scalar in [0,1) indicating that responses must be RDP with specific confidence.
latticeK: positive scalar integer-valued numeric specifying the lattice resolution. Defaults to (invalid) NA_integer_.
dims: positive scalar integer-valued numeric specifying the dimension of released function domain. Defaults to (invalid) NA_integer_.

Francesco Ald<c3><a0> and Benjamin I. P. Rubinstein. "The Bernstein Mechanism: Function Release under Differential Privacy", in Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'2017), pp. 1705-1711, Feb 2017.