Spruce is a system that enables scalable private outsourced retrieval by learning compact binary embeddings and using efficient Hamming-distance computation under a two‑server multi‑party computation protocol. It replaces costly corpus‑wide embedding scoring with a fixed‑radius protocol that avoids multi‑round candidate selection, and introduces private cluster pruning and a one‑core dealer to reduce computation and eliminate OT preprocessing bottlenecks. Across corpora of 383K–5.42M documents, Spruce maintains original search quality while achieving up to 31.5× higher throughput and reducing query times to a few seconds.
By Peichun Hua, Yunming Xiao
Spruce is a system that enables secure, private retrieval of large document collections outsourced to untrusted clouds by learning compact binary embeddings that preserve search quality while drastically reducing computation and communication. It replaces expensive corpus-wide embedding scoring with efficient Hamming-distance calculations under a two-server multi-party computation protocol, and introduces a fixed-radius protocol, private cluster pruning, and a one-core dealer to further cut latency and bandwidth usage. Across corpora ranging from 383K to 5.42M documents, Spruce maintains original search quality, achieving up to 6.7× faster full scans and 22.9× speedups with pruning, while retaining over 94% of the original NDCG.
arXiv:2609.36326v1 Announce Type: new
Abstract: Retrieval-augmented generation (RAG) hands the user's query to whoever hosts the corpus. We propose PILLAR, a Privacy-Preserving RAG (PPRAG) system bas...
By Truong Son Nguyen (Arizona State University), Daniel Blackley (George Mason University), Ni Trieu (Arizona State University), Evgenios M. Kornaropoulos (George Mason University)
arXiv:2606. 26373v1 Announce Type: cross Abstract: Dense embeddings power semantic search and retrieval-augmented generation, but embedding-inversion attacks can reconstruct source text from a vector: when a vector database leaks, the documents behind it leak too.
By Sergey Kurilenko
arXiv:2601. 07048v5 Announce Type: replace-cross Abstract: Approximate nearest neighbor search (ANNS) is a core problem in machine learning and information retrieval applications.
By Hunter McCoy, Zikun Wang, Prashant Pandey
The paper presents a practical private dense retrieval system that uses learned deep hashing as a private filter to generate a short candidate list for each query. Encrypted reranking and oblivious key transfer protect the exact query and final selection, allowing the system to match full‑corpus retrieval quality with only 200‑500 candidates. Experiments on five zero‑shot corpora and the 2.68M‑passage NQ corpus show minimal latency overhead and strong privacy guarantees.
By Peichun Hua, Danyang Chen, Junan Zhang, Haifeng Sun, Jingyu Wang, Diwen Xue, Mingyu Li, Yunming Xiao