@inproceedings{
andrew2023oneshot,
title={One-shot Empirical Privacy Estimation for Federated Learning},
author={Galen Andrew and Peter Kairouz and Sewoong Oh and Alina Oprea and Hugh McMahan and Vinith Suriyakumar},
booktitle={International Workshop on Federated Learning in the Age of Foundation Models in Conjunction with NeurIPS 2023},
year={2023},
url={https://openreview.net/forum?id=JmrHzzDiyI}
}
Privacy estimation techniques for differentially private (DP) algorithms are useful for comparing against analytical bounds, or to empirically measure privacy loss in settings where known analytical bounds are not tight. However, existing privacy auditing techniques usually make strong assumptions on the adversary (e.g., knowledge of  intermediate model iterates or the training data distribution), are tailored to specific tasks, model architectures, or DP algorithm, and/or require retraining the model many times (typically on the order of thousands). These shortcomings make deploying such techniques at scale difficult in practice, especially in federated settings where model training can take days or weeks. In this work, we present a novel ``one-shot'' approach that can systematically address these challenges, allowing efficient auditing or estimation of the privacy loss of a model during the same, single training run used to fit model parameters, and without requiring any *a priori* knowledge about the model architecture, task, or DP training algorithm. We show that our method provides provably correct estimates for the privacy loss under the Gaussian mechanism, and we demonstrate its performance on well-established FL benchmark datasets under several adversarial threat models.
One-shot Empirical Privacy Estimation for Federated Learning
Galen Andrew Peter Kairouz Sewoong Oh Alina Oprea Hugh McMahan Vinith Suriyakumar