Fast and Optimal Algorithms for Locally Private Mean Estimation

This software project accompanies the research paper, Faster Optimal Locally Private Mean Estimation via Random Projections.

We provide implementations for the algorithms in the above paper, along with code to reproduce all experiments.

Documentation

The main LDP mean estimation algorithms are implemented in the file PrivUnitAlgs.py. Code for reproducing the experiments can be found in experiments.py and experiments_MNIST.py for the general and MNIST experiments, respectively.

Running Experiments

Download the repository and install all required packages as listed in requirements.txt.

To run the general experiment (synthetic data), simply run the file experiments.py:

python experiments.py

For the MNIST experiments, first run the script train_MNIST_script.sh to produce the results of private training, then run experiments_MNIST.py to produce the plots:

bash train_MNIST_script.sh
python experiments_MNIST.py