arXiv:2602.00688v2 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) on sensitive datasets raises privacy concerns, as training data extraction (TDE) attacks can expose highly confidential information. Existing defenses against such attacks either lack formal privacy guarantees or incur substantial utility degradation. We observe that fine-tuning induces widespread probability shifts, yet preserving only a small subset of influential token-level deviations is sufficient; the remaining shifts can be aggressively smoothed with minimal impact on utility. Motivated by this
The increasing deployment of fine-tuned LLMs in sensitive applications necessitates robust privacy solutions to address growing concerns about data extraction attacks.
This research offers a method for protecting sensitive training data in LLMs without significant performance degradation, which is critical for broader enterprise and institutional adoption of AI.
The ability to formally protect LLMs from data extraction while maintaining utility mitigates a significant privacy risk, enabling more secure and responsible AI development and deployment.
- · Enterprises deploying LLMs with sensitive data
- · AI privacy solution providers
- · Developers of large language models
- · Sectors with strict data privacy regulations
- · Actors attempting training data extraction
- · Companies with poor data privacy practices
Increased trust and adoption of fine-tuned LLMs in privacy-sensitive domains.
Reduced regulatory hurdles for AI deployment in industries like healthcare and finance.
Accelerated development of domain-specific LLMs with proprietary datasets, fostering innovation in specialized AI applications.
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Read at arXiv cs.LG