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arXiv Machine Learning By Prakhar Ganesh, Yash More, Marco Romanelli, Ferdinando Fioretto, Golnoosh Farnadi

Privacy in Context Is Being Overlooked in the Age of General-Purpose LLMs

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The paper argues that as large language models (LLMs) expand, privacy concerns grow, yet current approaches tend to seek a single, universal privacy solution. It emphasizes that privacy is inherently contextual, and warns that a unified framework can give a misleading sense of security and ignore fundamental tensions among desired privacy properties. The authors propose focusing on application‑specific deployment, context‑specific evaluation, and improved privacy literacy as key future directions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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