arXiv:2606.09401v1 Announce Type: new Abstract: Recent work has applied differential privacy (DP) to adapt large language models (LLMs) for sensitive applications, offering theoretical guarantees. However, its practical effectiveness remains unclear, partly due to LLM pretraining, where overlaps and interdependencies with adaptation data can undermine privacy despite DP efforts. To analyze this issue in practice, we investigate privacy risks under DP adaptations in LLMs using state-of-the-art attacks such as robust membership inference and canary data extraction. We benchmark these risks by sy
The rapid deployment of LLMs into sensitive applications necessitates immediate validation of theoretical privacy guarantees against empirical attacks.
Ensuring the privacy of data used to adapt LLMs is critical for broad adoption in sectors like healthcare and finance, where data breaches carry severe consequences.
The focus is shifting from solely theoretical differential privacy guarantees to practical, empirical benchmarking of privacy effectiveness in real-world LLM adaptations.
- · Privacy-enhancing technology developers
- · LLM security researchers
- · Healthcare and financial sectors
- · LLM providers with weak privacy implementations
- · Organizations relying solely on theoretical privacy claims
Increased scrutiny and demand for robust empirical privacy testing in LLM development and deployment.
Development of new privacy-preserving fine-tuning techniques specifically designed to withstand advanced empirical attacks.
Potential for privacy-certified LLMs to become a competitive advantage, segmenting the market based on verifiable privacy claims.
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Read at arXiv cs.LG