New framework tackles privacy-utility trade-off in LLMs

Researchers have developed a new framework to address the privacy-utility trade-off in large language models (LLMs). Their analysis identified three key mechanisms: context-dependent utility, strategic adaptation, and combinatorial interplay, which explain how sanitization affects LLM performance. To implement these findings, they introduced an intent-driven local protection framework that uses a lightweight model called Veilmind-4B to dynamically extract, sanitize, and restore data. This approach achieves a low-leakage privacy point while maintaining significantly higher response utility compared to existing privacy-focused methods. AI

IMPACT This research could lead to LLMs that better protect user data without sacrificing performance.

RANK_REASON Research paper detailing a new framework for LLM privacy-utility trade-off. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• large-language models
• Veilmind-4B

• paper
• safety

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