covDataAccess

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

covDataAccess function

<dl><dt>x1, x2</dt>
<dd>Numeric vectors whose covariance is desired.</dd>
<dt>lower.bound1, lower.bound2</dt>
<dd>Real numbers giving the lower bounds of x1
and x2, respectively.</dd>
<dt>upper.bound1, upper.bound2</dt>
<dd>Real numbers giving the upper bounds of x1
and x2, respectively.</dd></dl>

Arguments

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

Differentially Private Covariance Data Access Function — covDataAccess

<dl>

<dt>x1, x2</dt>
<dd>Numeric vectors whose covariance is desired.</dd>


<dt>lower.bound1, lower.bound2</dt>
<dd>Real numbers giving the lower bounds of x1
and x2, respectively.</dd>


<dt>upper.bound1, upper.bound2</dt>
<dd>Real numbers giving the upper bounds of x1
and x2, respectively.</dd>

</dl>

covDataAccess: Differentially Private Covariance Data Access Function

Description

Usage

Value

Arguments

References

Examples