arXiv (OAI Expanded)
open access
Demystifying the Privacy-Utility Trade-off in LLM Interactions
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 gover
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Can Prompt Anonymity Protect Your Identity From LLM Providers?#1118976
- Learning to Refer: Client-Resolved Generation for Privacy-Aware Language Models#1117037
- PrivDrift: Auditing User-Secret Leakage Under Topic Drift in Active LLM Conversations#1078165
- Inferring Hidden User Models from the Behavior of Personalized LLM Agents#926696
- Removing the NEEDLE in the Haystack: Backdoor Removal in LLMs via Weight Orthogonalisation#1154971
Record · ID 1069950
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive