Passive Inference Attacks on Split Learning via Adversarial Regularization

核心概念

The authors propose a novel attack framework called Simulator Decoding with Adversarial Regularization (SDAR) that can effectively infer clients' private features and labels in both vanilla and U-shaped split learning settings, even in challenging scenarios where existing passive attacks struggle.

摘要

The paper introduces SDAR, a novel attack framework against split learning (SL) with an honest-but-curious server. SDAR leverages auxiliary data and adversarial regularization to learn a decodable simulator of the client's private model, which can effectively infer the client's private features under the vanilla SL, and both features and labels under the U-shaped SL.

The key highlights and insights are:

1. SDAR identifies an underexplored vulnerability of SL - the risks of information leakage from the parameters of the server's model g, which unintentionally memorizes information about the private data during joint training with the client's model f.

2. SDAR utilizes a novel adversarial regularization method to considerably improve the generalization performance of the simulator and decoder on the client's private data, achieving consistent and robust attack performance on less vulnerable models where existing attacks cannot work.

3. Extensive experiments on various real-world datasets and model architectures demonstrate that under challenging settings where existing passive attacks fail, SDAR achieves consistent and distinctive attack performance, and is even able to almost match the performance of active hijacking attacks.

4. The results reveal that inference attacks become more challenging with the increase of split level or the decrease of the client's model width. However, wider models are also more vulnerable to inference attacks due to their higher dimensional intermediate representations.

統計資料

The authors report the following key statistics:

On CIFAR-10 with ResNet-20 at split level 7, SDAR achieves private feature reconstruction with less than 0.025 mean squared error in both the vanilla and the U-shaped SL.
In the U-shaped SL setting, SDAR attains a label inference accuracy of over 98% on CIFAR-10.

引述

"SDAR leverages auxiliary data and adversarial regularization to learn a decodable simulator of the client's private model, which can effectively infer the client's private features under the vanilla SL, and both features and labels under the U-shaped SL."
"Notably, in challenging scenarios where existing passive attacks struggle to reconstruct the client's private data effectively, SDAR consistently achieves significantly superior attack performance, even comparable to active attacks."

從以下內容提煉的關鍵洞見

Passive Inference Attacks on Split Learning via Adversarial Regularization

by Xiaochen Zhu... 於 arxiv.org 09-11-2024

https://arxiv.org/pdf/2310.10483.pdf

深入探究

How can the client defend against SDAR attacks while preserving the efficiency benefits of split learning?

To defend against SDAR attacks while maintaining the efficiency benefits of split learning (SL), clients can implement several strategies:

Enhanced Model Complexity: Clients can increase the complexity of their models by adding more layers or increasing the width of the models. This approach can help obscure the intermediate representations sent to the server, making it more challenging for the server to accurately simulate the client’s model.

Noise Injection: Introducing noise into the intermediate representations before they are sent to the server can help protect sensitive information. This noise can be random perturbations that obscure the true data while still allowing the server to perform its training tasks effectively.

Adaptive Split Levels: Clients can dynamically adjust the split levels based on the sensitivity of the data being processed. For more sensitive data, a deeper split (more layers on the client side) can be employed, while less sensitive data can be processed with shallower splits.

Regularization Techniques: Clients can employ regularization techniques that limit the amount of information that can be inferred from the intermediate representations. Techniques such as dropout or weight decay can help in this regard.

Secure Multi-Party Computation (SMPC): Implementing SMPC protocols can allow clients to perform computations on their data without revealing it to the server. This can be integrated into the SL framework to enhance privacy without sacrificing efficiency.

Monitoring and Anomaly Detection: Clients can implement monitoring systems to detect unusual patterns in the server's behavior that may indicate an ongoing inference attack. This can help in taking timely actions to mitigate potential risks.

By combining these strategies, clients can enhance their defenses against SDAR attacks while still benefiting from the communication and computational efficiencies that split learning offers.

What are the potential implications of SDAR attacks on real-world applications of split learning, and how can the risks be mitigated?

The implications of SDAR attacks on real-world applications of split learning (SL) are significant, particularly in sensitive domains such as healthcare, finance, and personal data processing. The ability of an honest-but-curious server to infer private features and labels poses serious privacy risks, which can lead to:

Data Breaches: Sensitive information could be reconstructed by adversaries, leading to unauthorized access to personal data, which can have legal and ethical ramifications.

Loss of Trust: Users may lose trust in systems that utilize SL if they perceive that their data is not adequately protected, which can hinder the adoption of collaborative learning frameworks.

Regulatory Compliance Issues: Organizations may face challenges in complying with data protection regulations (e.g., GDPR, HIPAA) if they cannot ensure the privacy of client data during SL processes.

To mitigate these risks, organizations can adopt several measures:

Robust Security Protocols: Implementing strong encryption and secure communication protocols can help protect data in transit and reduce the risk of data leakage.

Regular Security Audits: Conducting regular audits and assessments of the SL framework can help identify vulnerabilities and ensure that appropriate security measures are in place.

User Education: Educating users about the privacy features and risks associated with SL can help them make informed decisions about their data sharing.

Adversarial Training: Incorporating adversarial training techniques can help improve the robustness of models against inference attacks, making it harder for adversaries to reconstruct private data.

Limit Data Sharing: Clients can limit the amount of data shared with the server to only what is necessary for training, thereby reducing the potential for sensitive information to be inferred.

By proactively addressing these implications and implementing robust risk mitigation strategies, organizations can enhance the security of split learning applications and protect user privacy.

Could the adversarial regularization techniques used in SDAR be adapted to improve the robustness of split learning models against inference attacks?

Yes, the adversarial regularization techniques used in SDAR can be adapted to improve the robustness of split learning (SL) models against inference attacks. Here are several ways in which these techniques can be beneficial:

Generalization Enhancement: By incorporating adversarial regularization, SL models can be trained to produce representations that are less sensitive to specific data distributions. This can help in making the model more robust against attempts to infer private data, as the representations will be less tailored to the auxiliary dataset.

Discriminator Training: The use of adversarial discriminators can be extended to monitor the outputs of the client’s model and the server’s model. By training discriminators to distinguish between real and synthetic outputs, the model can learn to produce outputs that are more resistant to inference attacks.

Dynamic Label Flipping: The technique of random label flipping, as used in SDAR, can be adapted to the training process of SL models. By introducing noise into the labels during training, the model can be encouraged to learn more generalized features, making it harder for adversaries to accurately infer private labels.

Multi-Discriminator Framework: Implementing a multi-discriminator framework can allow for more comprehensive monitoring of the model outputs. Different discriminators can focus on various aspects of the data, enhancing the model's ability to resist inference attacks.

Adversarial Training: Integrating adversarial training into the SL framework can help the model learn to withstand attacks by exposing it to adversarial examples during training. This can improve the model's robustness and reduce the likelihood of successful inference attacks.

Regularization of Intermediate Representations: Adversarial regularization can be applied directly to the intermediate representations shared with the server. By ensuring that these representations are indistinguishable from a broader distribution, the model can be made less vulnerable to reconstruction attacks.

By adapting these adversarial regularization techniques, practitioners can enhance the security and robustness of split learning models, making them more resilient to inference attacks while still benefiting from the efficiency and collaborative advantages of the SL framework.