ROKA: Robust Knowledge Unlearning against Adversaries
arXiv:2603.00436v1 Announce Type: new Abstract: The need for machine unlearning is critical for data privacy, yet existing methods often cause Knowledge Contamination by unintentionally damaging related knowledge. Such a degraded model performance after unlearning has been recently leveraged for new inference and backdoor attacks. Most studies design adversarial unlearning requests that require poisoning or duplicating training data. In this study, we introduce a new unlearning-induced attack model, namely indirect unlearning attack, which does not require data manipulation but exploits the consequence of knowledge contamination to perturb the model accuracy on security-critical predictions. To mitigate this attack, we introduce a theoretical framework that models neural networks as Neural Knowledge Systems. Based on this, we propose ROKA, a robust unlearning strategy centered on Neural Healing. Unlike conventional unlearning methods that only destroy information, ROKA constructively
arXiv:2603.00436v1 Announce Type: new Abstract: The need for machine unlearning is critical for data privacy, yet existing methods often cause Knowledge Contamination by unintentionally damaging related knowledge. Such a degraded model performance after unlearning has been recently leveraged for new inference and backdoor attacks. Most studies design adversarial unlearning requests that require poisoning or duplicating training data. In this study, we introduce a new unlearning-induced attack model, namely indirect unlearning attack, which does not require data manipulation but exploits the consequence of knowledge contamination to perturb the model accuracy on security-critical predictions. To mitigate this attack, we introduce a theoretical framework that models neural networks as Neural Knowledge Systems. Based on this, we propose ROKA, a robust unlearning strategy centered on Neural Healing. Unlike conventional unlearning methods that only destroy information, ROKA constructively rebalances the model by nullifying the influence of forgotten data while strengthening its conceptual neighbors. To the best of our knowledge, our work is the first to provide a theoretical guarantee for knowledge preservation during unlearning. Evaluations on various large models, including vision transformers, multi-modal models, and large language models, show that ROKA effectively unlearns targets while preserving, or even enhancing, the accuracy of retained data, thereby mitigating the indirect unlearning attacks.
Executive Summary
The article introduces ROKA, a robust unlearning strategy that mitigates knowledge contamination in machine learning models. ROKA constructs a Neural Knowledge System to model neural networks and proposes a Neural Healing approach to rebalance the model after unlearning, ensuring knowledge preservation. Evaluations on large models demonstrate ROKA's effectiveness in unlearning targets while preserving or enhancing accuracy, mitigating indirect unlearning attacks. This study provides a theoretical guarantee for knowledge preservation during unlearning, addressing a critical need for data privacy and security.
Key Points
- ▸ Introduction of ROKA, a robust unlearning strategy
- ▸ Proposal of Neural Healing approach to rebalance the model
- ▸ Evaluations on large models demonstrate ROKA's effectiveness
Merits
Theoretical Guarantee
ROKA provides a theoretical guarantee for knowledge preservation during unlearning, ensuring the model's accuracy is maintained or even enhanced.
Effectiveness in Mitigating Attacks
ROKA mitigates indirect unlearning attacks, which exploit knowledge contamination to perturb model accuracy.
Demerits
Complexity of Implementation
The implementation of ROKA may be complex, requiring significant modifications to existing machine learning frameworks.
Expert Commentary
The introduction of ROKA marks a significant advancement in machine unlearning, providing a robust strategy that balances the need for data privacy with the requirement for model accuracy. The theoretical guarantee for knowledge preservation during unlearning is a notable contribution, addressing a critical challenge in the field. However, the complexity of implementation may hinder widespread adoption, underscoring the need for further research and development. As machine learning continues to permeate various aspects of society, the development of ROKA highlights the importance of prioritizing data privacy and security in model design and deployment.
Recommendations
- ✓ Further research should focus on simplifying the implementation of ROKA, making it more accessible to practitioners and developers.
- ✓ Regulatory frameworks should be developed to address data privacy and security in machine learning, ensuring that models are designed and deployed with robust unlearning capabilities.