TreeCDF

An epsilon value for Differential Privacy

methods

mins

maxs

grans

datas

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.

Daniel Muise

CDF.PSIdekick

Evaluate Differentially Private Algorithms for Publishing
Cumulative Distribution Functions

TreeCDF function

Creates a Tree then a CDF

TreeCDF: Creates a Tree then a CDF

Description

Usage

Arguments

Value