sensitivity_mean

For mean queries with bounded data

internal

A tidy-style interface for applying differential privacy to data frames.
Provides pipe-friendly functions to add calibrated noise, compute private statistics,
and track privacy budgets using the epsilon-delta differential privacy framework.
Implements the Laplace mechanism (Dwork et al. 2006 <doi:10.1007/11681878_14>) and
the Gaussian mechanism for achieving differential privacy as described in Dwork and
Roth (2014) <doi:10.1561/0400000042>.

Thomas Tarler

tidydp

Tidy Differential Privacy

sensitivity_mean function

<dl><dt>lower</dt>
<dd>Lower bound of the data range</dd>
<dt>upper</dt>
<dd>Upper bound of the data range</dd>
<dt>n</dt>
<dd>Sample size</dd></dl>

Arguments

Calculate L2 Sensitivity for Mean Queries — sensitivity_mean

<dl>

<dt>lower</dt>
<dd>Lower bound of the data range</dd>


<dt>upper</dt>
<dd>Upper bound of the data range</dd>


<dt>n</dt>
<dd>Sample size</dd>

</dl>

sensitivity_mean: Calculate L2 Sensitivity for Mean Queries

Description

Usage

Value

Arguments