Private LR

This repository implements private linear regression models, comparing Differential Privacy (DP) to PAC (Probably Approximately Correct) Privacy and a non-private baseline. Differential privacy is a commonly accepted privacy notion, and the PAC Privacy framework is more recent.

Source Code

The source code is located in GitHub, at the link: https://github.com/hillaryyang/priv_lr

Features

We use Python to implement

• DP stochastic gradient descent using Opacus
• PAC Privacy framework

as well as non-private baselines.

Repository Structure

• datasets/: Contains the 3 datasets (Lenses, Concrete, Automobiles) used for training and testing
• np/: Code for non private baseline
• dp/: Code for DP linear regression
  • dp/private.py: Updates training objects using Opacus' privacy engine
  • dp/grid_search/: Contains scripts for grid search for DP SGD optimal hyperparameters
• pac/: Code for PAC linear regression
  • pac/private.py: Privatization functions such as noise estimation for membership privacy
  • pac/alpha_search/: Scripts for tuning hyperparameter alpha for regularized linear regression
• data_loader.py: Utility script for loading/preprocessing datasets

Installation

1. Clone the repository:

git clone https://github.com/hillaryyang/priv_lr.git
cd priv_lr

2. For MacOS, create and activate a virtual environment

python -m venv ~/env
source ~/env/bin/activate  

3. Install dependencies

pip install -r requirements.txt

Usage

After inputting the desired hyperparameters in the Python files, run the code for DP/PAC/non-private:

• DP: python3 dp/<dataset>_dp.py
• PAC: python3 pac/<dataset>_pac.py
• Non-private: python3 np/<dataset>_np.py

Results are printed to the command line.