meanDataAccess

This function performs the data access step in the computation of a
differentially private mean. The true values are computed using
<code><a href="/link/mean?package=DPpack&version=0.2.0" data-mini-rdoc="DPpack::mean">mean</a></code>, while the sensitivities are calculated based on
bounded and unbounded differential privacy Kifer2011DPpack
according to the theoretical values Liu2019bDPpack. For the
mean, the sensitivities based on bounded and unbounded differential privacy
are identical, so only one value is returned.

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

meanDataAccess function

<dl><dt>x</dt>
<dd>Dataset whose mean is desired.</dd>
<dt>lower.bound</dt>
<dd>Scalar representing the global or public lower bound on
values of x.</dd>
<dt>upper.bound</dt>
<dd>Scalar representing the global or public upper bound on
values of x.</dd></dl>

Arguments

This function performs the data access step in the computation of a
differentially private mean. The true values are computed using
<code><a href='https://rdrr.io/r/base/mean.html'>mean</a></code>, while the sensitivities are calculated based on
bounded and unbounded differential privacy Kifer2011DPpack
according to the theoretical values Liu2019bDPpack. For the
mean, the sensitivities based on bounded and unbounded differential privacy
are identical, so only one value is returned.

Differentially Private Mean Data Access Function — meanDataAccess

<dl>

<dt>x</dt>
<dd>Dataset whose mean is desired.</dd>


<dt>lower.bound</dt>
<dd>Scalar representing the global or public lower bound on
values of x.</dd>


<dt>upper.bound</dt>
<dd>Scalar representing the global or public upper bound on
values of x.</dd>

</dl>

meanDataAccess: Differentially Private Mean Data Access Function

Description

Usage

Value

Arguments

References

Examples