Stealing Client Data in Federated Learning: A Novel Attack Evading Detection

Khái niệm cốt lõi

A novel attack framework called SEER can effectively steal client data from federated learning models, even with large batch sizes and secure aggregation, while avoiding detection by clients.

Tóm tắt

The paper presents a thorough study of the client-side detectability of existing malicious server (MS) attacks in federated learning (FL). It demonstrates that both boosted analytical and example disaggregation attacks are detectable using principled checks, such as the newly introduced disaggregation signal-to-noise ratio (D-SNR) metric.
The authors then propose a novel attack framework called SEER that satisfies the necessary requirements for practical MS attacks. SEER avoids the pitfalls of prior attacks by using a secret decoder to disaggregate the data in a hidden space, and jointly optimizing the decoder and the shared model with SGD on auxiliary data.
Extensive experiments show that SEER can effectively steal client data from realistic convolutional networks, even with large batch sizes up to 512 and under secure aggregation. SEER outperforms prior state-of-the-art MS attacks in terms of reconstruction quality and undetectability.
The paper highlights the importance of studying attack detectability and represents a promising step towards assessing the true vulnerability of federated learning in real-world settings.

Thống kê

The paper presents several key metrics to evaluate the performance of the proposed SEER attack:

Rec (%): The fraction of good reconstructions (PSNR > 19) out of all attacked batches.
PSNR-Top: The average PSNR across the top 1/e ≈ 37% of the best reconstructed batches.
PSNR-All: The average PSNR across all attacked batches.

Trích dẫn

"Malicious server (MS) attacks have enabled the scaling of data stealing in federated learning to large batch sizes and secure aggregation, settings previously considered private."
"We thoroughly study the question of client-side detectability of MS attacks. We demonstrate that while boosted analytical and example disaggregation attacks pose a real threat as zero-day exploits, now that their key principles are known, all current (and future) attacks from these two classes are client-side detectable in a principled manner, bringing into question their practicality."
"We propose SEER, a novel attack framework which satisfies all requirements based on malicious training of the shared model with a secret server-side decoder. SEER is harder to detect by design as it does not rely on honest attacks, avoiding previous pitfalls."

Thông tin chi tiết chính được chắt lọc từ

Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning

by Kost... lúc arxiv.org 04-16-2024

https://arxiv.org/pdf/2306.03013.pdf

Yêu cầu sâu hơn

How can the proposed SEER attack be extended to other data modalities beyond images, such as text or tabular data

The SEER attack, which focuses on stealing data from federated learning models, can indeed be extended to other data modalities beyond images, such as text or tabular data. To adapt SEER for text data, the secret decoder and reconstructor components would need to be modified to handle text embeddings and reconstructions. The property thresholding mechanism used in SEER could be adjusted to identify specific text features or patterns for disaggregation. Additionally, the shared model training process would need to be tailored to encode text data effectively in the gradient space. For tabular data, the property selection process could involve identifying key features or combinations of features that are unique to individual records within the batch. The secret decoder and reconstructor would then be designed to work with tabular data structures, extracting and reconstructing specific rows or entries. Overall, by customizing the components of SEER to suit the characteristics of text and tabular data, the attack could be extended successfully to these alternative data modalities.

What are the potential limitations of SEER, and how could future work address them to further improve the attack's effectiveness and practicality

While SEER presents a novel and effective approach to data stealing in federated learning, there are potential limitations that future work could address to enhance the attack's effectiveness and practicality. One limitation is the reliance on a specific property thresholding mechanism for identifying target data points within a batch. Future work could explore more robust and adaptive methods for property selection that are less dependent on manual tuning. Additionally, the scalability of SEER to larger and more complex datasets could be a challenge, requiring optimizations in training and inference processes. Another limitation is the potential for detection by clients through advanced monitoring techniques. Future iterations of SEER could focus on developing more sophisticated evasion strategies to bypass client-side detection mechanisms. Moreover, the generalizability of SEER to diverse model architectures and data distributions could be improved to ensure its applicability in various FL scenarios. By addressing these limitations, future work could enhance SEER's capabilities and make it a more potent and versatile attack strategy in federated learning environments.

Given the serious privacy implications of the SEER attack, what novel defense mechanisms could be developed to reliably detect and mitigate such malicious server attacks in federated learning

In response to the serious privacy implications of the SEER attack and similar malicious server attacks in federated learning, novel defense mechanisms could be developed to reliably detect and mitigate such threats. One approach could involve the implementation of anomaly detection algorithms that monitor model behavior and gradients for unusual patterns indicative of data theft. By establishing baseline behaviors and detecting deviations, these algorithms could flag suspicious activities for further investigation. Another defense mechanism could involve the integration of secure multi-party computation (SMPC) or homomorphic encryption techniques to protect sensitive data during the federated learning process. These cryptographic methods would ensure that data remains encrypted and secure, even in the presence of malicious servers. Furthermore, the development of robust auditing and accountability frameworks could help track and trace unauthorized access or data breaches in federated learning systems. By combining these defense strategies with continuous monitoring and proactive measures, organizations can strengthen their defenses against malicious server attacks like SEER and safeguard user privacy in federated learning environments.