Exploiting Gradient Leakage to Infer Private Labels in Vertical Federated Graph Neural Networks

Conceptos Básicos

The core message of this paper is to propose a novel zero-background-knowledge label inference attack, called BlindSage, that can effectively extract private labels from a vertical federated learning setting using Graph Neural Networks (GNNs) without requiring any prior knowledge about the model architecture or the number of classes.

Resumen

The paper presents BlindSage, a label inference attack against node-level classification using vertical federated GNNs. The key highlights and insights are:

BlindSage is a novel zero-background-knowledge attack that can infer private labels without requiring any prior information about the model architecture or the number of classes. This is in contrast to existing attacks that rely on having access to a subset of labeled data.

The attack exploits the gradients returned by the server during the federated training process to estimate the association between the node embeddings and the true labels. This allows the attacker to perform the attack stealthily without affecting the main federated learning task.

The authors evaluate BlindSage under different GNN architectures (GCN, GAT, GraphSAGE) and datasets, demonstrating its effectiveness. In the basic knowledge scenario, the attack achieves nearly 100% accuracy in most cases. Even in the limited and no knowledge scenarios, the accuracy remains above 90%.

The authors propose an early stopping strategy for the attack based on monitoring the trend of the local model gradients. This helps the attacker determine the optimal time to stop the attack for maximum performance.

The authors also investigate different techniques for the attacker to approximate the server model architecture, showing that the matching loss between the real and adversarial gradients is a crucial factor for the attack's success.

The authors test potential mitigation strategies and find that while some methods can reduce the attack's effectiveness, they also significantly compromise the performance of the main classification task.

Estadísticas

The average value of the local model gradients is a key indicator of the attack's performance. Spikes in the gradient magnitude often lead to a decline in the attack accuracy.
The matching loss between the real and adversarial gradients is a crucial factor in determining the best approximation of the server model architecture for the attacker.

Citas

"Our proposed attack, BlindSage, provides impressive results in the experiments, achieving nearly 100% accuracy in most cases. Even when the attacker has no information about the used architecture or the number of classes, the accuracy remains above 90% in most instances."
"We relax the assumptions about the attackers' knowledge regarding the model's architecture and the number of classes, and we see that our attack can still reach a very high attack accuracy, i.e., more than 90%."

Ideas clave extraídas de

Label Inference Attacks against Node-level Vertical Federated GNNs

by Marco Arazzi... a las arxiv.org 04-19-2024

https://arxiv.org/pdf/2308.02465.pdf

Consultas más profundas

How can the vertical federated learning framework be further strengthened to provide robust protection against label inference attacks like BlindSage without significantly compromising the main classification task's performance

To enhance the robustness of the vertical federated learning framework against label inference attacks like BlindSage, several strategies can be implemented:

Noise Addition: Introducing noise to the gradients shared during the federated learning process can help obfuscate sensitive information, making it harder for attackers to extract labels accurately. By adding controlled noise to the gradients, the attacker's ability to infer labels is diminished without significantly impacting the main classification task's performance.

Gradient Masking: Implementing techniques to mask or distort the gradients shared between the server and clients can prevent attackers from extracting meaningful information. By manipulating the gradients before sharing them, the attack surface for label inference is reduced, enhancing the security of the federated learning process.

Dynamic Gradient Updates: Implementing a dynamic mechanism to update the gradients shared during training can add an extra layer of security. By altering the timing or frequency of gradient updates, the system can prevent attackers from exploiting consistent patterns in the gradients to infer labels accurately.

Adversarial Training: Incorporating adversarial training techniques can help the model become more resilient to attacks. By training the model against potential attacks like BlindSage, the system can learn to defend against such threats and improve its overall security posture.

Model Architecture Variability: Introducing variability in the model architectures used by different clients can make it harder for attackers to generalize their attacks. By diversifying the architectures within the federated learning framework, the system can mitigate the impact of attacks like BlindSage.

By implementing a combination of these strategies, the vertical federated learning framework can be strengthened to provide robust protection against label inference attacks while maintaining the main classification task's performance.

What other types of sensitive information, beyond just labels, could be inferred by an attacker exploiting gradient leakage in vertical federated learning settings

In addition to labels, attackers exploiting gradient leakage in vertical federated learning settings could potentially infer other sensitive information, such as:

Feature Importance: By analyzing the gradients shared during training, attackers could infer the importance of different features in the classification task. This information could reveal insights into the underlying data distribution and potentially compromise the privacy of sensitive features.

Data Distribution: Gradient leakage could also provide attackers with information about the distribution of data across different clients. This could lead to privacy breaches and unauthorized access to data that should remain confidential.

Model Architecture: Attackers could potentially infer details about the model architecture used in the federated learning framework by analyzing the gradients. This information could be exploited to launch targeted attacks on the system or gain insights into the model's inner workings.

Training Patterns: By analyzing the patterns in the gradients shared during training, attackers could infer details about the training process, including convergence behavior, learning rates, and optimization strategies. This information could be leveraged to optimize attacks or exploit vulnerabilities in the system.

Overall, gradient leakage in vertical federated learning settings poses a significant risk of exposing various sensitive information beyond just labels, highlighting the importance of robust security measures to protect against such attacks.

Could the techniques used in BlindSage be adapted to attack other types of federated learning settings, such as horizontal federated learning or federated transfer learning, where the attacker's knowledge and access to information may differ

The techniques used in BlindSage could be adapted to attack other types of federated learning settings, such as horizontal federated learning or federated transfer learning, with some modifications to account for differences in the attacker's knowledge and access to information:

Horizontal Federated Learning: In a horizontal setting, where clients share the same features but have different examples, the attacker's strategy may need to be adjusted to exploit this data distribution. By leveraging gradient leakage and model approximation techniques, the attacker could potentially infer labels or other sensitive information across the shared feature space.

Federated Transfer Learning: In federated transfer learning, where clients have different feature spaces and instances, the attacker may need to adapt the attack strategy to account for these differences. By incorporating techniques to approximate the target model and infer labels based on gradient leakage, the attacker could potentially compromise the privacy and security of the federated transfer learning process.

By tailoring the techniques used in BlindSage to suit the specific characteristics of horizontal federated learning and federated transfer learning settings, attackers could potentially launch effective label inference attacks and extract sensitive information from the federated models.