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.

Version: 1.2
Imports: Rcpp (≥ 0.12.6)
LinkingTo: Rcpp
Published: 2016-08-19
Author: Daniel Muise [aut,cre], Kobbi Nissim [aut], Georgios Kellaris [aut]
Maintainer: Daniel Muise <dmuise at stanford.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: CDF.PSIdekick results

Downloads:

Reference manual: CDF.PSIdekick.pdf
Package source: CDF.PSIdekick_1.2.tar.gz
Windows binaries: r-devel: CDF.PSIdekick_1.2.zip, r-release: CDF.PSIdekick_1.2.zip, r-oldrel: CDF.PSIdekick_1.2.zip
OS X El Capitan binaries: r-release: CDF.PSIdekick_1.2.tgz
OS X Mavericks binaries: r-oldrel: CDF.PSIdekick_1.2.tgz
Old sources: CDF.PSIdekick archive

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