Privacy-Preserving LLM Embedding Transmission for End-Cloud Collaboration
New privacy-preserving methods for LLM embedding transmission aim to prevent cloud providers from reconstructing user queries during retrieval-augmented generation—relevant for enterprises splitting inference between edge and cloud.
Summary written by editorial AI · Source link below
arXiv:2503.12896v2 Announce Type: replace Abstract: Recent studies improve on-device language model (LM) inference through end-cloud collaboration, where the end device retrieves useful information from cloud databases to enhance local processing, known as Retrieval-Augmented Generation (RAG). Typically, to retrieve information from the cloud while safeguarding privacy, the end device transforms original data into embeddings with a local embedding model. However, the recently emerging Embedding
Editorial Analysis
European enterprises using cloud-augmented RAG must prevent embedding-based query reconstruction to satisfy GDPR data-minimisation requirements and protect sensitive business queries.
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