The Elusive Pursuit of Replicating PATE-GAN: Benchmarking, Auditing, Debugging

Synthetic data created by differentially private (DP) generative models is increasingly used in real-world settings. In this context, PATE-GAN has emerged as a popular algorithm, combining Generative Adversarial Networks (GANs) with the private training approach of PATE (Private Aggregation of Teacher Ensembles). In this paper, we analyze and benchmark six open-source PATE-GAN implementations, including three by (a subset of) the original authors. First, we shed light on architecture deviations and empirically demonstrate that none replicate the utility performance reported in the original paper. Then, we present an in-depth privacy evaluation, including DP auditing, showing that all implementations leak more privacy than intended and uncovering 17 privacy violations and 5 other bugs. Our codebase is available from https://github.com/spalabucr/pategan-audit.