Spruce: Scalable Private Outsourced Retrieval Using Compact Embeddings
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 secure implementation takes minutes and about 90 GB of communication per query. Even recent optimized systems require 10--22 seconds. We propose Spruce (Scalable Private Outsourced Retrieval Using Compact Embeddings), which co-designs representat...
| Authors | Peichun Hua, Yunming Xiao |
|---|---|
| Primary category | cs.CR |
| Published | 3 September 2026 |
| Last updated at source | 3 October 2026 |
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