This repo adapts code from https://github.com/xiyangl3/adp-estimator/ to apply the techniques in that repo's accompanying paper, Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases to examining PyDP.
- To run the experiments, you need to have the following libraries installed:
- python = 3.6
- numpy
- scipy
-
The coefficient of best polynomial approximation are pre-computed and stored as ".mat" file. The coefficient of Chebyshev polynomials of the first kind are stored as ".npy" file.
-
To get the coefficient of best polynomial approximation, you need to install Chebfun in Matlab through http://www.chebfun.org/
-
To get the coefficient of Chebyshev polynomials, you need to install:
- sympy
You are encouraged to cite orginal paper for acedamic research:
@inproceedings{liu2019minimax,
title={Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases},
author={Liu, Xiyang and Oh, Sewoong},
booktitle={Advances in Neural Information Processing Systems},
pages={2414--2425},
year={2019}
}
MIT.