SDempiric

The vector output of a non-differentially private CDF
computation (cumulative count bins)

The vector output of a differentially private CDF
computation (cumulative count bins)

Optionally add additional parameters. This is primarily used to allow automated
execution of varied diagnostic functions.


Calculates the standard deviation across bins between the 
   non-private CDF and the DP-CDF


Designed by and for the community of differential privacy algorithm developers. It can be used to empirically evaluate and visualize Cumulative Distribution Functions incorporating noise that satisfies differential privacy, with numerous options made to streamline collection of utility measurements across variations of key parameters, such as epsilon, domain size, sample size, data shape, etc. Developed by researchers at Harvard PSI.

SDempiric: Calculate the std. dev. on a DPCDF.

Description

Usage

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Value

Examples