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CDF.PSIdekick: Evaluate Differentially Private Algorithms for Publishing Cumulative Distribution Functions
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.
Getting started
Browse package contents
Package details |
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| Author | Daniel Muise [aut,cre], Kobbi Nissim [aut], Georgios Kellaris [aut] |
| Maintainer | Daniel Muise <dmuise@stanford.edu> |
| License | GPL (>= 2) |
| Version | 1.2 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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