MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models
arXiv:2602.23798v1 Announce Type: new Abstract: Machine unlearning for large language models often faces a privacy dilemma in which strict constraints prohibit sharing either the server's parameters or the client's forget set. To address this dual non-disclosure constraint, we propose MPU, an algorithm-agnostic privacy-preserving Multiple Perturbed Copies Unlearning framework that primarily introduces two server-side modules: Pre-Process for randomized copy generation and Post-Process for update aggregation. In Pre-Process, the server distributes multiple perturbed and reparameterized model instances, allowing the client to execute unlearning locally on its private forget set without accessing the server's exact original parameters. After local unlearning, the server performs Post-Process by inverting the reparameterization and aggregating updates with a harmonic denoising procedure to alleviate the impact of perturbation. Experiments with seven unlearning algorithms show that MPU ach
arXiv:2602.23798v1 Announce Type: new Abstract: Machine unlearning for large language models often faces a privacy dilemma in which strict constraints prohibit sharing either the server's parameters or the client's forget set. To address this dual non-disclosure constraint, we propose MPU, an algorithm-agnostic privacy-preserving Multiple Perturbed Copies Unlearning framework that primarily introduces two server-side modules: Pre-Process for randomized copy generation and Post-Process for update aggregation. In Pre-Process, the server distributes multiple perturbed and reparameterized model instances, allowing the client to execute unlearning locally on its private forget set without accessing the server's exact original parameters. After local unlearning, the server performs Post-Process by inverting the reparameterization and aggregating updates with a harmonic denoising procedure to alleviate the impact of perturbation. Experiments with seven unlearning algorithms show that MPU achieves comparable unlearning performance to noise-free baselines, with most algorithms' average degradation well below 1% under 10% noise, and can even outperform the noise-free baseline for some algorithms under 1% noise. Code is available at https://github.com/Tristan-SHU/MPU.
Executive Summary
This article proposes the MPU framework, a novel algorithm-agnostic approach to secure and privacy-preserving knowledge unlearning for large language models. MPU addresses the dual non-disclosure constraint by introducing two server-side modules: Pre-Process for randomized copy generation and Post-Process for update aggregation. Experiments demonstrate that MPU achieves comparable unlearning performance to noise-free baselines with minimal degradation. The proposed framework has significant implications for the deployment of large language models in sensitive applications, such as healthcare and finance, where data privacy is paramount. While the MPU framework shows promise, further research is needed to evaluate its scalability and generalizability across different model architectures and unlearning algorithms.
Key Points
- ▸ MPU addresses the dual non-disclosure constraint in machine unlearning for large language models.
- ▸ The framework introduces two server-side modules: Pre-Process and Post-Process.
- ▸ Experiments demonstrate comparable unlearning performance to noise-free baselines with minimal degradation.
Merits
Strength in Addressing Privacy Concerns
MPU provides a novel solution to the dual non-disclosure constraint, enabling secure and privacy-preserving knowledge unlearning for large language models.
Algorithm-Agnostic Approach
The framework is designed to be algorithm-agnostic, allowing it to be integrated with various unlearning algorithms and model architectures.
Experimental Validation
Experiments demonstrate the effectiveness of the MPU framework, showing comparable unlearning performance to noise-free baselines with minimal degradation.
Demerits
Limited Scalability Evaluation
The article does not provide a comprehensive evaluation of the MPU framework's scalability across different model architectures and unlearning algorithms.
Lack of Real-World Deployment
The article does not provide any real-world deployment scenarios or case studies to demonstrate the practical applicability of the MPU framework.
Uncertainty Surrounding Noise Tolerance
The article does not provide a detailed analysis of the MPU framework's noise tolerance, which may be a limiting factor in certain applications.
Expert Commentary
The MPU framework is a significant contribution to the field of machine unlearning for large language models. The proposed approach addresses the dual non-disclosure constraint, enabling secure and privacy-preserving knowledge unlearning. However, further research is needed to evaluate the framework's scalability and generalizability across different model architectures and unlearning algorithms. Additionally, the article highlights the need for policy updates and regulations to ensure the secure and private deployment of large language models in sensitive applications.
Recommendations
- ✓ Further research is needed to evaluate the MPU framework's scalability and generalizability across different model architectures and unlearning algorithms.
- ✓ The proposed framework should be integrated with various unlearning algorithms and model architectures to demonstrate its flexibility and adaptability.
- ✓ Policy updates and regulations should be developed to ensure the secure and private deployment of large language models in sensitive applications.