DPMechGaussian-class

DPMechGaussian

show,DPMechGaussian-method

an instance of class <code>DPMech</code>.

object

A class that implements the Gaussian mechanism of differential privacy,
for privatizing numeric vector releases.

An implementation of major general-purpose mechanisms for privatizing
statistics, models, and machine learners, within the framework of differential
privacy of Dwork et al. (2006) <doi:10.1007/11681878_14>. Example mechanisms
include the Laplace mechanism for releasing numeric aggregates, and the
exponential mechanism for releasing set elements. A sensitivity sampler
(Rubinstein & Alda, 2017) <arXiv:1706.02562> permits sampling target
non-private function sensitivity; combined with the generic mechanisms, it
permits turn-key privatization of arbitrary programs.

Benjamin Rubinstein

diffpriv

Easy Differential Privacy

Francesco Ald<c3><a0>

DPMechGaussian-class function

<ul>
<li><code>show</code>: automatically prints the object.</li>
</ul>

Methods (by generic)

<dl class="dl-horizontal">
<dt><code>sensitivity</code></dt><dd>non-negative scalar numeric L2 target sensitivity.
Defaults to <code>Inf</code> for use with <code>sensitivitySampler()</code>.</dd></dl><dt><code>target</code></dt><dd>the target non-private function to be privatized, takes lists.
Defaults to a constant function. Gaussian mechanism assumes functions that
release numeric vectors of fixed dimension <code>dims</code>.</dd><dt><code>gammaSensitivity</code></dt><dd><code>NA_real_</code> if inactive, or scalar in [0,1)
indicating that responses must be RDP with specific confidence.</dd><dt><code>dims</code></dt><dd>positive scalar numeric dimension of responses. Defaults to
<code>NA_integer_</code> for use with <code>sensitivitySampler()</code> which can
probe <code>target</code> to determine dimension.</dd>

DPMechGaussian-class: An S4 class for the Gaussian mechanism of differential privacy.

Description

Usage

Arguments

Methods (by generic)

Slots

References