L2empiric

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 L2 (squared error) area between the 
   non-private CDF and the dpCDF


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.

L2empiric: Calculate the empirical L2norm between two CDFs.

Description

Usage

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Examples