dpCDFtesting-package

dpCDFtesting


  This package's primary contribution is the function CDFtest, which is used to visualize and collect large empirical diagnostic data on the performance of user-defined dpCDF implementations. It also includes 4 simple dpCDF implementations.
  


Differential Privacy

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.

dpCDFtesting-package: Comprehensively evaluate and visualize the output of dpCDF-generating algorithm implementations. dp = Differential Privacy

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