# G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators Source: [G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:51:06.318Z Authors: Yunhui Long, Boxin Wang, Zhuolin Yang, Bhavya Kailkhura, Aston Zhang, Carl A. Gunter, Bo Li Published: 2019-06-21T21:42:23.000Z ## Abstract Recent advances in machine learning have largely benefited from the massive accessible training data. However, large-scale data sharing has raised great privacy concerns. In this work, we propose a novel privacy-preserving data Generative model based on the PATE framework (G-PATE), aiming to train a scalable differentially private data generator that preserves high generated data utility. Our approach leverages generative adversarial nets to generate data, combined with private aggregation among different discriminators to ensure strong privacy guarantees. Compared to existing approaches, G-PATE significantly improves the use of privacy budgets. In particular, we train a student data generator with an ensemble of teacher discriminators and propose a novel private gradient aggregation mechanism to ensure differential privacy on all information that flows from teacher discriminators to the student generator. In addition, with random projection and gradient discretization, the proposed gradient aggregation mechanism is able to effectively deal with high-dimensional gradient vectors. Theoretically, we prove that G-PATE ensures differential privacy for the data generator. Empirically, we demonstrate the superiority of G-PATE over prior work through extensive experiments. We show that G-PATE is the first work being able to generate high-dimensional image data with high data utility under limited privacy budgets ($\\epsilon \\le 1$). Our code is available at https://github.com/AI-secure/G-PATE. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on CelebA-H (test)](): Image Classification; CelebA-H (test); Accuracy (ε=1): 49.85. - [Image Classification on CelebA-G (test)](): Image Classification; CelebA-G (test); Accuracy (ε=10): 68.97. - [Image Classification on MNIST (test) (Accuracy with Epsilon Constraints)](): Image Classification; MNIST (test); Accuracy (ε=10): 80.92. - [Image Classification on FashionMNIST](): Image Classification; FashionMNIST; Accuracy: 50. ## Original sources and implementation [Original paper]() [PDF]()