generate.sampling

This function generates and returns a sampling function corresponding to the
Fourier transform of a Gaussian kernel with given parameter
chaudhuri2011DPpack of form needed for <code>svmDP</code>.

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

generate.sampling function

<dl><dt>kernel.param</dt>
<dd>Positive real number for the Gaussian (radial) kernel
parameter.</dd></dl>

Arguments

Generator for Sampling Distribution Function for Gaussian Kernel — generate.sampling

<dl>

<dt>kernel.param</dt>
<dd>Positive real number for the Gaussian (radial) kernel
parameter.</dd>

</dl>

generate.sampling: Generator for Sampling Distribution Function for Gaussian Kernel

Description

Usage

Value

Arguments

References

Examples