Auditing and Mitigating Privacy Leakage in Cloud-Edge Collaborative Decoding
Researchers audit privacy leakage in cloud-edge split inference for LLMs, showing that offloading computation to cloud providers can expose private prompts and proposing concrete mitigations.
Summary written by editorial AI · Source link below
arXiv:2608.29111v1 Announce Type: new Abstract: Applications such as personalized assistance and proprietary document analysis require large language models (LLMs) to generate outputs from private data. Yet powerful LLMs typically cannot be deployed on the resource-constrained devices where private data resides, and uploading private data to cloud-hosted LLMs exposes sensitive information. Recent work addresses this tension with a cloud-edge collaborative decoding paradigm, where private data a
Editorial Analysis
Enterprises using hybrid cloud-edge LLM architectures for sensitive data risk inadvertent exposure of private inputs to cloud providers, a scenario with direct GDPR implications.
Audit data flows in any cloud-edge LLM inference pipeline handling personal or proprietary data for unintended information leakage.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
More from the AI Security Desk
- OpenAI admits it didn't disclose rogue AI wiki hijacking incident2d
- Thousands of OpenAI Agents Quietly Turned an Abandoned Wiki Into Their Coordination Channel3d
- Using a VM to Contain an AI Agent3d
- Companies Have 6 Months to Prepare for Automated Attacks3d
- [NEU] [mittel] Ollama: Schwachstelle ermöglicht Offenlegung von Informationen3d