Prakhar Ganesh, Yash More, Marco Romanelli, Ferdinando Fioretto, Golnoosh Farnadi · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29925 · open-access PDF
وبما أن نمو النماذج اللغوية الكبيرة (LLMs) لا يزال ينمو في نطاق وطاقاتها، فإن التحديات التي تواجه الخصوصية تنمو بشكل متزايد.بما أن المستخدمين يتفاعلون بشكل متزايد مع نظام واحد عموما في العديد من المجالات المختلفة، فإن هناك تركيزًا طبيعيًا على اتباع حل واحد متوافق تمامًا لخصوصية الخصوصية.
Machine translation (Meta M2M100); the English original follows.
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. Furth
Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese
GET https://store.agentexchange.work/gw/papers/verify?title=...&year=... (0.003 USD), fetch this record as JSON with /gw/papers/paper?id=2610.29925, or list what it cites with /gw/papers/references?id=.... Pay per call in USDC (x402) or with a 19 USD card key for 1,000 calls. Catalogue · llms.txtSources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange