Spruce: Scalable Private Outsourced Retrieval Using Compact Embeddings
Spruce enables scalable private vector retrieval for RAG on untrusted clouds via compact embeddings, addressing a key barrier to confidential enterprise AI deployments.
Summary written by editorial AI · Source link below
arXiv:2609.03376v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has made dense retrieval over large document collections a standard building block. Organizations increasingly outsource vector indexes to untrusted clouds, exposing proprietary corpora and user queries. Cryptographic protection is challenging because each query searches corpus-scale state, causing computation, correlated randomness, and communication to grow with the corpus. At million-document scale, a naive
Editorial Analysis
Enterprises outsourcing RAG workloads risk exposing proprietary corpora and user queries; practical private-retrieval schemes could unlock secure cloud-based AI without data exposure.
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.
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