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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

Chinese

随着大型语言模型(LLMs)在范围和能力上不断增长,隐私问题正在上升。随着用户在许多不同的背景下越来越多地与一个单一的通用系统进行互动,有一个自然的倾向,追求一个单一的适合所有解决方案的隐私。不幸的是,这忽略了几十年的隐私研究的关键见解:隐私本质上是背景的。在这篇论文中,我们认为现有的工作在即时隐私正在向设计一个统一的框架的尝试倾斜,这极具误导性,并可以培育一种虚假的安全感。此外,我们认为,即使在未来的工作中,在这个方向上也需要对冲突各种可取的隐私特性的基本紧张。因此,对统一的框架的不断尝试需要重新考虑。我们建议三条关键的

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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