Protecting Proprietary Transformer Models from Unauthorized Access in Edge Deployment

Concepts de base

A plug-and-play approach called TransLinkGuard that protects proprietary transformer models from model stealing attacks when deployed on edge devices.

Résumé

The content discusses the security challenges of deploying proprietary large language models (LLMs) on edge devices, where the models are exposed as white-box and vulnerable to model stealing (MS) attacks. Existing defense mechanisms fail to provide effective protection that satisfies four critical properties: maintaining protection after physical copying, authorizing model access at the request level, safeguarding against runtime reverse engineering, and achieving high security with negligible runtime overhead.

To address these challenges, the authors propose TransLinkGuard, a novel approach that deploys a "locked" transformer model on the edge device and an authorization module in a secure environment (e.g., TEE). The key innovation is a lightweight permutation-based authorization mechanism that allows only authorized requests to correctly compute the permuted model layers. This approach ensures proactive protection, request-level authorization, runtime security, and high efficiency.

Extensive experiments show that TransLinkGuard outperforms existing partial TEE-shielded execution (PTSE) solutions in terms of security guarantee and efficiency, achieving black-box-level security with negligible overhead. The authors also demonstrate that TransLinkGuard maintains the original model's accuracy without any degradation.

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Idées clés tirées de

TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deployment

by Qinfeng Li,Z... à arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11121.pdf

Questions plus approfondies

How can TransLinkGuard's authorization mechanism be extended to protect other types of neural network architectures beyond transformers

TransLinkGuard's authorization mechanism can be extended to protect other types of neural network architectures beyond transformers by adapting the permutation-based approach to suit the specific characteristics of different models. For instance, for convolutional neural networks (CNNs), the authorization mechanism could involve permuting the filter weights in each convolutional layer. This would require designing a permutation strategy that disrupts the positional information of the filters, similar to how TransLinkGuard permutes the weights in transformer layers. Additionally, for recurrent neural networks (RNNs), the authorization process could involve permuting the recurrent weights in each recurrent layer to achieve access control. By customizing the permutation strategy for each type of neural network architecture, TransLinkGuard's principles can be applied effectively to protect a wide range of proprietary AI models.

What are the potential limitations or drawbacks of the permutation-based approach used in TransLinkGuard, and how could they be addressed in future work

One potential limitation of the permutation-based approach used in TransLinkGuard is the computational overhead introduced by the permutation process. Permuting the weights of each layer in a neural network can increase the computational complexity, leading to higher inference times and resource requirements. To address this limitation, future work could focus on optimizing the permutation process to reduce the computational overhead. This optimization could involve exploring more efficient permutation algorithms or techniques to streamline the authorization mechanism without compromising security. Additionally, research could be conducted to investigate the trade-offs between security and efficiency in the permutation-based approach, aiming to strike a balance that ensures robust protection without significant performance degradation.

Given the importance of protecting intellectual property in the AI ecosystem, how might TransLinkGuard's principles be applied to safeguard other types of proprietary AI models beyond language models

To apply TransLinkGuard's principles to safeguard other types of proprietary AI models beyond language models, the core concept of authorization through permutation can be adapted to suit the specific requirements of different model architectures. For image recognition models, the authorization mechanism could involve permuting the convolutional filters or fully connected layers to prevent unauthorized access to the model's parameters. Similarly, for reinforcement learning models, the authorization process could focus on permuting the policy or value function networks to ensure secure inference. By tailoring the permutation-based approach to the unique characteristics of each type of AI model, TransLinkGuard's principles can be extended to protect a diverse range of proprietary models in the AI ecosystem.