phi.gaussian

This function maps an input data row x with a given prefilter to an output
value in such a way as to approximate the Gaussian kernel
chaudhuri2011DPpack.

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

phi.gaussian function

<dl><dt>x</dt>
<dd>Vector or matrix corresponding to one row of the dataset X.</dd>
<dt>theta</dt>
<dd>Randomly sampled prefilter vector of length n+1, where n is the
length of x.</dd></dl>

Arguments

Transform Function for Gaussian Kernel Approximation — phi.gaussian

<dl>

<dt>x</dt>
<dd>Vector or matrix corresponding to one row of the dataset X.</dd>


<dt>theta</dt>
<dd>Randomly sampled prefilter vector of length n+1, where n is the
length of x.</dd>

</dl>

phi.gaussian: Transform Function for Gaussian Kernel Approximation

Description

Usage

Value

Arguments

References

Examples