Malicious Data Reconstruction Attack in Vertical Federated Learning Leveraging Label Information

Core Concepts

The adversary leverages label information and a discriminator with auxiliary classifier to effectively reconstruct target clients' private features while evading detection by current defense mechanisms in vertical federated learning.

Abstract

The paper introduces DMAVFL, a novel defense-penetrating malicious data reconstruction attack in vertical federated learning (VFL). Unlike traditional approaches that utilize a discriminator, DMAVFL incorporates training a discriminator with auxiliary classifier (DAC), which helps to significantly enhance the effectiveness in embedding distribution transfer and attack performance, as well as make the malicious gradients indistinguishable from the ones with honest training.

The key steps of DMAVFL are:

1. Pretraining: The adversary pretrains an encoder, a decoder, and its bottom model to achieve high reconstruction performance on an auxiliary dataset.

2. Malicious gradient generation: The adversary freezes the encoder and replaces the conventional top model with the DAC. The DAC is instrumental in transferring the embedding distribution from the encoder into the target model and integrating label information through classification processes, ensuring the malicious training is indistinguishable from honest training.

3. Data reconstruction: The adversary leverages the trained decoder to reconstruct private features of target clients, from embeddings uploaded by passive clients and the adversary's local embedding.

The comprehensive experiments demonstrate that DMAVFL significantly outperforms existing attacks, and successfully circumvents state-of-the-art defenses for malicious attacks. Additional ablation studies and evaluations on other defenses further underscore the robustness and effectiveness of DMAVFL.

Stats

The paper does not provide any specific numerical data or statistics to support the key claims.

Quotes

"The key idea is to integrate a discriminator with auxiliary classifier that takes a full advantage of the label information (which was completely ignored in previous attacks): on one hand, label information helps to better characterize embeddings of samples from distinct classes, yielding an improved reconstruction performance; on the other hand, computing malicious gradients with label information better mimics the honest training, making the malicious gradients indistinguishable from the honest ones, and the attack much more stealthy."

Key Insights Distilled From

Leveraging Label Information for Stealthy Data Stealing in Vertical Federated Learning

by Duanyi Yao,S... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19582.pdf

Deeper Inquiries

How can the proposed DMAVFL attack be extended to handle scenarios with more than two clients in the VFL system

To extend the DMAVFL attack to scenarios with more than two clients in the VFL system, the adversary can modify the malicious gradient generation process to incorporate embeddings from multiple passive clients. Instead of focusing on a single set of embeddings from one passive client, the adversary can aggregate embeddings from all passive clients in the batch to compute the malicious loss and gradients. This would involve updating the DAC to handle multiple sets of embeddings and labels, ensuring that the attack strategy considers the diverse feature sources across all clients. By adapting the DMAVFL approach to handle multiple clients, the adversary can enhance the stealthiness and effectiveness of the attack in a multi-client VFL setting.

What are the potential limitations or drawbacks of the DMAVFL approach, and how can they be addressed in future research

One potential limitation of the DMAVFL approach is the reliance on the auxiliary dataset for pretraining and malicious gradient generation. This dependency on external data may not always be feasible or practical in real-world scenarios where access to such datasets is limited. To address this limitation, future research could explore techniques for generating synthetic auxiliary data or leveraging transfer learning to adapt pretraining to the specific VFL dataset without the need for an external auxiliary dataset. Additionally, the DMAVFL approach may face challenges in scaling to larger datasets or more complex VFL systems due to computational constraints. Future research could focus on optimizing the attack strategy for scalability and efficiency in handling larger datasets and more clients in the VFL system.

What other types of information, beyond just labels, could the adversary leverage to further enhance the stealthiness and effectiveness of the data reconstruction attack in VFL

Beyond labels, the adversary could leverage additional information such as class probabilities, instance weights, or feature importance scores to further enhance the stealthiness and effectiveness of the data reconstruction attack in VFL. By incorporating class probabilities, the adversary can tailor the malicious gradients to focus on specific classes or instances, improving the reconstruction performance for targeted features. Instance weights can be used to prioritize certain samples during training, allowing the adversary to emphasize the reconstruction of critical or sensitive data points. Feature importance scores can guide the attack towards extracting and reconstructing the most relevant and informative features, enhancing the overall quality of the reconstructed data. By integrating these additional types of information into the attack strategy, the adversary can optimize the data reconstruction process and increase the success rate of stealing private features in VFL.