SOURCE-LINKED INTELLIGENCE

Demystifying the Privacy-Utility Trade-off in LLM Interactions

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

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, which first establishes when to sanitize by reveal

Read original source ↗ Open in workspace

recordType

paper

region

Global