Spruce: Scalable Private Outsourced Retrieval Using Compact Embeddings

arXiv:2609.03376 · cs.CR, cs.IR, cs.LG · Submitted 2026-09-03 · Read on arXiv

cs.CR, cs.IR, cs.LG

Submitted: 2026-09-03

Updated: 2026-10-03

Comments: 23 pages, 10 tables, 6 figures

Code: https://github.com/osu-crypto/libOTe

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Embeddings — 39× in this paper · explained in 2 episodes
• Pruning — 31× in this paper · explained in 4 episodes
• RAG — 23× in this paper · explained in 3 episodes
• Calibration — 15× in this paper · explained in 7 episodes
• Quantization — 15× in this paper · explained in 12 episodes
• Transformer — 11× in this paper · explained in 2 episodes
• Core — 8× in this paper · explained in 2 episodes
• Hamming Distance — 8× in this paper · explained in 2 episodes
• Retrieval-Augmented Generation — 7× in this paper
• Benchmark — 5× in this paper · explained in 2 episodes
• Clustering — 4× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Differential Privacy — 3× in this paper · explained in 4 episodes
• Distillation — 3× in this paper · explained in 2 episodes
• Empirical Methods in Natural Language Processing — 3× in this paper
• Differentially Private — 2× in this paper · explained in 3 episodes
• Retrieval-Augmented Generation (RAG) — 2× in this paper · explained in 16 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Differential Privacy (DP) — 1× in this paper · explained in 7 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• National Institute of Standards and Technology — 1× in this paper
• Predictive Coding — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 3 episodes

Sources

• Wally: Batched Private Nearest Neighbor Search at Scale
• MESS: Fast and Private Semantic Search on Multi-Graph HNSW
• CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims
• Retrieval-Augmented Generation for Large Language Models: A Survey
• A Survey on Deep Text Hashing: Efficient Semantic Text Retrieval with Binary Representation
• Pointing the Way, Hiding the Destination: Practical Private Dense Retrieval at Scale
• P$^2$RAG: Efficient Privacy-Preserving RAG Service Supporting Arbitrary Top-$k$ Retrieval
• PIR-RAG: A System for Private Information Retrieval in Retrieval-Augmented Generation
• Text Embeddings by Weakly-Supervised Contrastive Pre-training
• Representation Learning with Contrastive Predictive Coding

Related papers

• SoK: AI-Augmented Binary Reversing
• Relaxed Sender Anonymity for CBDC Interbank Settlement: A Zero-Knowledge Approach on Permissioned EVM
• Calibration-Family Overfit: Why Trusted Sabotage Monitors Don't Transfer Across Lineages
• Efficient Fuzzy PSI under One-Sided Assumptions
• Sealing the Audit-Runtime Gap for LLM Skills
• Token Composition: A Graph Based on EVM Logs