DPMechBernstein-class {diffpriv}

An S4 class for the Bernstein mechanism of differential privacy.

Description

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.

Usage

## 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)

Arguments

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.

Value

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_\infty of the functions on a lattice.

Methods (by generic)

• show: automatically prints the object.

• releaseResponse: releases Bernstein mechanism responses.

• sensitivityNorm: measures target sensitivity.

Slots

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_.

References

Francesco Aldà 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.

Examples

## See the bernstein vignette