The Art of Mixology: Mixup-based Obfuscation for Privacy-Preserving Split Learning in Large Language Models

Problem
Existing privacy-preserving split learning methods for large language models (LLMs) struggle with a trade-off between utility, privacy, efficiency, and stability. These methods often lead to significant utility degradation, are susceptible to advanced data reconstruction attacks, and incur high computational and communication costs. Furthermore, they exhibit inconsistent performance across various tasks. This paper addresses these gaps by proposing a novel framework, MIXGUARD, which aims to enhance privacy without compromising model utility. Notably, this work is a preprint and has not undergone peer review.

Method
MIXGUARD employs a mixup-based approach that integrates three key mechanisms: token-level obfuscation, representation-level obfuscation, and adaptive gradient perturbation. The framework begins with the construction of a lightweight calibration model trained on a public dataset, which refines the approximated target representation. This model is then utilized during the privacy-preserving fine-tuning phase on private data. The architecture is designed to maintain the integrity of useful learning signals while mitigating privacy risks. The authors conduct extensive experiments across four classification tasks and four text generation tasks, utilizing various LLM families, model sizes, and fine-tuning strategies to validate the effectiveness of MIXGUARD.

Results
MIXGUARD demonstrates comparable utility to non-split training baselines across all evaluated tasks. Specifically, it achieves a utility retention rate of over 90% when compared to traditional training methods. In terms of privacy protection, MIXGUARD outperforms existing split learning defense mechanisms against state-of-the-art data reconstruction attacks, achieving a reduction in reconstruction accuracy by up to 30% compared to the best baseline. Additionally, the framework exhibits robustness under adaptive attack scenarios, maintaining performance stability across different tasks and model architectures.

Limitations
The authors acknowledge that while MIXGUARD significantly enhances privacy and utility, it may still be vulnerable to certain advanced attack vectors not covered in their experiments. Additionally, the reliance on a public dataset for calibration may introduce biases that could affect the generalizability of the model. The computational overhead associated with the calibration model and the mixup-based mechanisms may also limit scalability in extremely resource-constrained environments.

Why it matters
MIXGUARD represents a significant advancement in the field of privacy-preserving machine learning, particularly for LLMs, by effectively balancing the trade-offs between utility and privacy. The proposed framework not only enhances the security of split learning but also provides a robust solution for resource-constrained users. This work lays the groundwork for future research in privacy-preserving techniques and could influence the design of more secure LLM training paradigms, as discussed in related literature on privacy in machine learning, available on arXiv.