Newest papers

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

Prakhar Ganesh, Yash More, Marco Romanelli, Ferdinando Fioretto, Golnoosh Farnadi · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29925 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

大規模な言語モデル(LLMs)が範囲と能力で増加し続けるにつれて、プライバシーの懸念は増加しています。ユーザーが多くの異なる文脈で単一の一般的な目的のシステムとますます相互作用しているため、プライバシーに対する一つのサイズに合わせたすべてのソリューションを追求する自然な傾向があります。残念ながら、これは、プライバシー研究の数十年にわたる重要な洞察を無視しています:プライバシーは本質的に文脈的です。この論文では、既存のプライバシーの迅速な作業は、統一されたフレームワークを設計しようとする試みに向かい、非常に誤導し、安全性の誤った感を育成する可能性があります。さらに、私たちは、この方向での将来の作業でさえ、プライバシー

Machine translation (Meta M2M100); the English original follows.

Abstract

As large language models (LLMs) continue to grow in scope and capability, privacy concerns are on the rise. Since users increasingly interact with a single general-purpose system across many distinct contexts, there is a natural tendency to pursue a one-size-fits-all solution to privacy. Unfortunately, this overlooks a crucial insight from decades of privacy research: privacy is inherently contextual. In this paper, we argue that existing work in prompt privacy is drifting towards attempts to design a unified framework, which is highly misleading and can foster a false sense of security. Furthermore, we argue that even future work in this direction needs to wrestle with fundamental tensions across various desirable properties of prompt privacy. Thus, such continued attempts towards a unified framework need to be reconsidered. We recommend three critical paths forward: application-specific deployment of LLMs, context-specific evaluation of privacy frameworks, and better privacy literacy.

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Topics: Scope (computer science), Internet privacy, Context (archaeology), Information privacy, Software deployment, Privacy by Design

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