Privacy by Proximity: How Nearest Neighbors Made In-Context Learning Differentially Private
Opening — Why this matters now As large language models (LLMs) weave themselves into every enterprise workflow, a quieter issue looms: the privacy of the data used to prompt them. In‑context learning (ICL) — the art of teaching a model through examples in its prompt — is fast, flexible, and dangerously leaky. Each query could expose confidential examples from private datasets. Enter differential privacy (DP), the mathematical armor for sensitive data — except until now, DP methods for ICL have been clumsy and utility‑poor. ...