histogramDataAccess

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

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

histogramDataAccess function

<dl><dt>x</dt>
<dd>Numeric vector from which the histogram will be formed..</dd>
<dt>breaks</dt>
<dd>Identical to the argument with the same name from
<code><a href="/link/hist?package=DPpack&version=0.2.0" data-mini-rdoc="DPpack::hist">hist</a></code>.</dd>
<dt>mechanism</dt>
<dd>String indicating which mechanism to use for differential
privacy. If the 'Laplace' mechanism is chosen, l1 sensitivities are
returned. If the 'Gaussian' or 'analytic' mechanisms are chosen, l2
sensitivities are returned.</dd></dl>

Arguments

This function performs the data access step in the computation of a
differentially private histogram. The true values are computed using
<code><a href='https://rdrr.io/r/graphics/hist.html'>hist</a></code>, while the sensitivities are calculated based on
bounded and unbounded differential privacy Kifer2011DPpack
according to the theoretical values Liu2019bDPpack.

Differentially Private Histogram Data Access Function — histogramDataAccess

<dl>

<dt>x</dt>
<dd>Numeric vector from which the histogram will be formed..</dd>


<dt>breaks</dt>
<dd>Identical to the argument with the same name from
<code><a href='https://rdrr.io/r/graphics/hist.html'>hist</a></code>.</dd>


<dt>mechanism</dt>
<dd>String indicating which mechanism to use for differential
privacy. If the 'Laplace' mechanism is chosen, l1 sensitivities are
returned. If the 'Gaussian' or 'analytic' mechanisms are chosen, l2
sensitivities are returned.</dd>

</dl>

histogramDataAccess: Differentially Private Histogram Data Access Function

Description

Usage

Value

Arguments

References

Examples