Demystifying the Privacy-Utility Trade-off in LLM Interactions
Quick summary
arXiv:2609.10992v1 Announce Type: new Abstract: The integration of Large Language Models into daily tasks relies on context-rich instructions, inevitably exposing sensitive user information. Current privacy-preserving methods typically employ context-agnostic static rules, causing severe utility degradation. However, the specific mechanisms governing how sanitization impacts downstream performance remain largely underexplored. To address this, we conduct a systematic analysis to deconstruct the privacy-utility trade-off, uncovering three underlying mechanisms: (1) Context-Dependent Utility, wh
Key takeaways
- arXiv:2609.10992v1 Announce Type: new Abstract: The integration of Large Language Models into daily tasks relies on context-rich instructions, inevitably exposing sensitive user information.
- Current privacy-preserving methods typically employ context-agnostic static rules, causing severe utility degradation.
- However, the specific mechanisms governing how sanitization impacts downstream performance remain largely underexplored.
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “Demystifying the Privacy-Utility Trade-off in LLM Interactions” may reshape data collection, model training, output accountability and market access.
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