Efficient One-shot Empirical Privacy Estimation for Federated Learning

Core Concepts

A novel "one-shot" approach for efficiently auditing or estimating the privacy loss of a model during the same, single training run used to fit model parameters, without requiring any a priori knowledge about the model architecture, task, or DP training algorithm.

Abstract

The paper presents a novel "one-shot" approach for efficiently estimating the privacy loss of a model trained using Differentially Private Federated Averaging (DP-FedAvg). The key ideas are:

Augment the training dataset with k "canary" clients who contribute random model updates. Measure the cosine similarity between the final model and each canary update.
Use the distribution of canary cosines to estimate the privacy loss (ε) of the final model, without requiring retraining the model many times or having prior knowledge of the model architecture, task, or DP algorithm.
The method can be applied during the same, single training run used to train the model parameters, and has negligible impact on model quality.
The method provably recovers the true analytical ε in the case of a single application of the Gaussian mechanism, and is shown to perform well on well-established FL benchmark datasets under several adversarial threat models.
The method can be used to explore how privacy loss changes as aspects of the training protocol change, for which no tight theoretical analysis is known, such as limiting client participation.

Stats

The model has 4.1M parameters and is trained on the Stackoverflow word prediction dataset with 341k clients.
The model is trained for 2048 rounds with 167 clients per round, amounting to a single epoch over the data.
The noise multiplier (ratio of noise to clipping norm) is varied from 0.0496 to 0.2317, corresponding to analytical ε estimates from 300 down to 30.

Quotes

"Our method flexibly estimates the privacy loss in the context of arbitrary participation patterns, for example passing over the data in epochs, or the difficult-to-characterize de facto pattern of participation in a deployed system, which may include techniques intended to amplify privacy such as limits on client participation within temporal periods such as one day."
"We argue that when the model dimensionality is sufficiently high, such crafting is unnecessary, since a randomly chosen canary update will already be essentially orthogonal to the true updates with high probability."

Key Insights Distilled From

One-shot Empirical Privacy Estimation for Federated Learning

by Galen Andrew... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2302.03098.pdf

Deeper Inquiries

How can the proposed method be extended to provide tighter privacy guarantees, beyond just estimating the privacy loss

The proposed method can be extended to provide tighter privacy guarantees by incorporating more sophisticated techniques for analyzing the canary data. One approach could be to use advanced machine learning algorithms to detect subtle patterns in the canary updates that indicate potential privacy leaks. By leveraging techniques such as anomaly detection, clustering, or deep learning, the method could identify specific instances where the model may be memorizing sensitive information. This would allow for a more targeted and precise estimation of privacy loss, leading to tighter guarantees.
Additionally, the method could be enhanced by introducing adaptive strategies for selecting and presenting the canaries during training. By dynamically adjusting the characteristics of the canaries based on the model's behavior and the training data, the method could adapt to changing privacy threats and provide more accurate estimates of privacy loss. This adaptive approach would enable the method to respond effectively to evolving adversarial models and improve the overall robustness of the privacy estimation process.

What are the limitations of the canary-based approach, and how could it be improved to handle more sophisticated adversarial models

The canary-based approach has certain limitations that could be addressed to handle more sophisticated adversarial models. One limitation is the assumption that the canaries are independent and uniformly distributed, which may not always hold in real-world scenarios. To improve the approach, the canary selection process could be optimized to ensure a more diverse and representative set of canaries. This could involve using techniques such as active learning or reinforcement learning to select canaries that are more likely to reveal privacy vulnerabilities in the model.
Another limitation is the reliance on cosine similarity as the test statistic for measuring privacy loss. While cosine similarity is a useful metric for detecting memorization, it may not capture all types of privacy leaks. To overcome this limitation, the approach could be extended to incorporate more advanced similarity metrics or feature representations that capture a broader range of privacy threats. By diversifying the set of test statistics used to evaluate privacy loss, the approach could provide a more comprehensive analysis of the model's privacy vulnerabilities.

How can the insights from this work on privacy estimation be applied to develop new DP-FL algorithms that provably provide stronger privacy guarantees

The insights from this work on privacy estimation can be applied to develop new DP-FL algorithms that provably provide stronger privacy guarantees. One approach is to integrate the privacy estimation method into the training process of DP-FL algorithms to continuously monitor and adjust the level of privacy protection. By dynamically optimizing the privacy parameters based on real-time privacy estimates, the algorithms can adapt to changing privacy threats and ensure robust privacy guarantees throughout the training process.
Furthermore, the insights from this work can inform the design of novel DP-FL algorithms that incorporate advanced privacy-preserving techniques. For example, the method's focus on canary-based privacy estimation could inspire the development of new privacy mechanisms that leverage canary data to enhance privacy protection in FL settings. By integrating canary-based privacy estimation techniques into the algorithm design, researchers can create more effective and efficient DP-FL algorithms that provide stronger privacy guarantees and mitigate the risk of privacy breaches.