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
arXiv:2603. 06660v2 Announce Type: replace-cross Abstract: Approximate Nearest Neighbor Search (ANNS) is fundamental to modern AI applications.
By Kejing Lu, Zhenpeng Pan, Jianbin Qin, Yoshiharu Ishikawa, Chuan Xiao
arXiv:2605. 26903v2 Announce Type: replace-cross Abstract: Structured data is well handled by gradient-boosted decision trees (GBDT), which are usually trained on vertically partitioned features across mutually distrustful parties.
By Chenyu Huang, Fan Zhang, Minxin Du, Sherman S. M. Chow, Huangxun Chen, Huaming Rao, Danqing Huang, Bo Qian, Peng Chen
Hosted retrieval-augmented generation (RAG) and semantic search allow users to query valuable provider-held corpora, raising two competing demands: to hide each query and chosen result, yet reveal onl...
CertEx introduces a GPU‑based exact k‑nearest‑neighbor search that relies on certified exclusion, a technique that conservatively eliminates vectors using low‑precision bounds before computing exact distances. By storing a half‑precision lower‑bounding projection and embedding a rounding certificate into the tensor‑core dot product, CertEx only evaluates distances for a small subset of candidates, guaranteeing exact results. Experiments on sixteen datasets with up to 100 million vectors show that CertEx achieves 7.1× to 18.0× the throughput of the fastest cuVS search on an H100 and outperforms quantized and graph indexes when exactness is required.
By Dongfang Zhao
arXiv:2606. 26664v1 Announce Type: cross Abstract: Existing homomorphic encryption (HE)-based GNN systems adopt a graph-centric paradigm that couples per-query cost to global graph size, limiting evaluations to at most ~20k nodes and making them incompatible with dynamic, large-scale financial graphs.
By Ngoc Bao Anh Le, Thai T. Vu, John Le, Heath Cooper, Jun Shen
arXiv:2609. 02143v1 Announce Type: cross Abstract: Most vector databases rely on graph-based indexes, notably HNSW and Vamana, for approximate nearest neighbor search.
By Sajad Faghfoor Maghrebi, Navid Eslami, Niv Dayan