smoothVector2

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.


Forces DP-CDFs into the nearest monotonic vector
   (by euclidean distance minimization).


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.

smoothVector2: Enforce monotnocity on a vector.

Description

Usage

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Value

Examples