/Smooth_Random_Trees

A Differentially-Private Random Decision Forest using Smooth Sensitivity

Primary LanguagePython

Smooth_Random_Trees

A Differentially-Private Random Decision Forest using Smooth Sensitivity

Based on the algorithm proposed in:

S. Fletcher and M. Z. Islam. Differentially Private Random Decision Forests using Smooth Sensitivity. axXiv preprint, 2016, https://arxiv.org/abs/1606.03572

The algorithm requires:

  • training and testing data
  • a list of the categorical (i.e. discrete) attributes
  • the number of trees to build
  • the total privacy budget

The algorithm outputs:

  • a differentially-private classification model
  • six class variables that describe the model and its performance on the testing data

You can redistribute them and/or modify them under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. The main requirement of which is that you cite the above paper. Thanks!