calibrateAnalyticGaussianMechanism

Calibrate a Gaussian perturbation for differential privacy using the analytic
Gaussian mechanism Balle2018DPpack.

internal

An implementation of common statistical analysis and models with
differential privacy (Dwork et al., 2006a) <doi:10.1007/11681878_14>
guarantees. The package contains, for example, functions providing
differentially private computations of mean, variance, median, histograms,
and contingency tables. It also implements some statistical models and
machine learning algorithms such as linear regression (Kifer et al., 2012)
<https://proceedings.mlr.press/v23/kifer12.html>
and SVM (Chaudhuri et al., 2011)
<https://jmlr.org/papers/v12/chaudhuri11a.html>. In addition, it implements
some popular randomization mechanisms, including
the Laplace mechanism (Dwork et al., 2006a)
<doi:10.1007/11681878_14>, Gaussian mechanism (Dwork et al., 2006b)
<doi:10.1007/11761679_29>, analytic Gaussian mechanism (Balle & Wang, 2018)
<https://proceedings.mlr.press/v80/balle18a.html>, and exponential mechanism
(McSherry & Talwar, 2007) <doi:10.1109/FOCS.2007.66>.

Spencer Giddens

DPpack

Differentially Private Statistical Analysis and Machine Learning

Fang Liu

calibrateAnalyticGaussianMechanism function

<dl><dt>epsilon</dt>
<dd>Positive real number defining the epsilon privacy parameter.</dd>
<dt>delta</dt>
<dd>Positive real number defining the delta privacy parameter.</dd>
<dt>sensitivity</dt>
<dd>Real number corresponding to the l2-global sensitivity.</dd>
<dt>tol</dt>
<dd>Error tolerance for binary search.</dd></dl>

Arguments

Calibrate Analytic Gaussian Mechanism — calibrateAnalyticGaussianMechanism

<dl>

<dt>epsilon</dt>
<dd>Positive real number defining the epsilon privacy parameter.</dd>


<dt>delta</dt>
<dd>Positive real number defining the delta privacy parameter.</dd>


<dt>sensitivity</dt>
<dd>Real number corresponding to the l2-global sensitivity.</dd>


<dt>tol</dt>
<dd>Error tolerance for binary search.</dd>

</dl>

calibrateAnalyticGaussianMechanism: Calibrate Analytic Gaussian Mechanism

Description

Usage

Value

Arguments

References