Privacy Attacks in Federated Learning

Our first post in the series introduced the concept of federated learning—an approach for training AI models on distributed data by sharing model updates instead of training data. At first glance, federated learning seems to be a perfect fit for privacy since it completely avoids sharing data.

However, recent work on privacy attacks has shown that it’s possible to extract a surprising amount of information about the training data, even when federated learning is used. These techniques fall into two major categories: attacks that target the model updates shared during training, and attacks that extract data from the AI model after training has finished.

This post summarizes known attacks and provides recent examples from the research literature. The primary goal of the UK-US PETs Prize Challenges was to develop practical defenses that augment federated learning frameworks to prevent these attacks; future posts in this series will describe these defenses in detail.

Attacks on Model Updates

In federated learning, each participant submits model updates instead of raw training data during the training process. In our example from the last post - in which a consortium of banks wants to train an AI model to detect fraudulent transactions - the model updates may consist of updates to the model’s parameters (the components of the model that control how its predictions are made) rather than raw data about financial transactions. At first glance, the model updates may appear to convey no information about financial transactions.

However, recent research has demonstrated that it’s often possible to extract raw training data from model updates. One early example came from the work of Hitaj et al., who showed that it was possible to train a second AI model to reconstruct training data based on model updates. One example of their results appears in Figure 1: the top row contains training data used to train a model that recognizes handwritten digits, and the bottom row contains data extracted from model updates by their attack.

Later work by Zhu et al. suggests that this kind of attack is possible for many different kinds of models and their corresponding model updates. Figure 2 contains examples from four different AI models, showing that the attack is able to extract nearly-perfect approximations of the original training data from the model updates.

How to fix it!

Attacks on model updates suggest that federated learning alone is not a complete solution for protecting privacy during the training process. Many defenses against such attacks focus on protecting the model updates during training, so that the organization that aggregates the model updates does not have access to individual updates.

Privacy-enhancing technologies that protect the model updates during training are often said to provide input privacy - they prevent the adversary from learning anything about the inputs (i.e. the model updates) to the system. Many approaches for input privacy, including approaches used in the UK-US PETs Prize Challenges, rely on creative applications of cryptography. We’ll highlight several of these solutions throughout this blog series.

Attacks on Trained Models

The second major class of attacks target the trained AI model after training has finished. The model is the output of the training process, and often consists of model parameters that control the model’s predictions. This class of attacks attempts to reconstruct the training data from the model’s parameters, without any of the additional information available during the training process. This may sound like a more difficult challenge, but recent research has demonstrated that such attacks are feasible.

AI models based on deep learning are particularly susceptible to the extraction of training data from trained models because deep neural networks seem to memorize their training data in many cases. Researchers are still unsure about why this memorization happens, or whether it is strictly necessary to train effective AI models. From a privacy perspective, however, this kind of memorization is a significant problem. Recent work by Haim et al. demonstrated the feasibility of extracting training data from an AI model trained to recognize objects in images; an example appears in Figure 3.

The challenge of memorized training data seems to be even worse for larger, more complex models - including popular large language models (LLMs) like ChatGPT and image generation models like DALL-E. Figure 4 shows an example of extracting training data from an image generation model using an attack developed by Carlini et al.; Figure 5 shows an example of extracting training data from a large language model using an attack by Carlini et al.

How to fix it!

Attacks on trained models show that trained models are vulnerable, even when the training process is completely protected. Defenses against such attacks focus on controlling the information content of the trained model itself, to prevent it from revealing too much about the training data.

Privacy-enhancing technologies that protect the trained model are often said to provide output privacy - they prevent the adversary from learning anything about the training data from the system’s outputs (i.e. the trained model). The most comprehensive approach for ensuring output privacy is called differential privacy, and is the subject of a previous NIST blog series and new draft guidelines. Many of the solutions developed in the UK-US PETs Prize Challenges leverage differential privacy to defend against attacks on the trained model, which we’ll highlight later in this blog series.

As always, we hope to hear from you with any questions and feedback. Please contact us at pets [at] cdei.gov.uk (pets[at]cdei[dot]gov[dot]uk) or privacyeng [at] nist.gov (privacyeng[at]nist[dot]gov).

Coming Up Next

In our next post, we’ll introduce one of the key issues for federated learning: distribution of the data among the participating entities.