Authors: Marcel Neunhoeffer, Zhiwei Steven Wu, Cynthia Dwork

Associated Paper: Private Post-GAN Boosting (ICLR 2021)

Overview

This repository contains the replication code for our ICLR 2021 paper “Private Post-GAN Boosting.” The method addresses a fundamental challenge in differentially private synthetic data generation: GAN training often fails to converge due to privacy-protective noise, leading to poor quality synthetic data.

The Method

Private Post-GAN Boosting (Private PGB) combines samples from the full sequence of generators obtained during GAN training—not just the final generator. Using a game-theoretic formulation with multiplicative weights, the method finds an equilibrium between a synthetic data player and a distinguisher.

Key Insight: While individual generators during training may be poor, a weighted mixture from different training epochs can produce high-quality synthetic data with formal differential privacy guarantees.

Experiments Included

  • Toy data (Gaussian mixtures)
  • MNIST
  • US Census data
  • Titanic dataset

Core Components

  • Private Multiplicative Weights method (Hardt and Rothblum, 2010)
  • Discriminator Rejection Sampling (Azadi et al., 2019)
  • Game-theoretic reweighting of generated samples