用深度哈希做私有过滤器：可扩展的实用私有稠密检索

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Abstract: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 only the documents that the user is authorized to receive. Existing cryptographic approaches either make this costly by processing the entire corpus for every query, or sacrifice quality for efficiency by scanning a few clusters. We repurpose learned deep hashing as a private filter: a randomized binary code points the provider to a short candidate list, while encrypted reranking and oblivious key transfer protect the precise query and final selection. This shortlist short-circuits full-corpus cryptographic search without sacrificing retrieval quality: with 200-500 candidates, it closely matches full-corpus retrieval across five zero-shot corpora spanning 25K to 5.4M documents. On the full 2.68M-passage NQ corpus over a 10-Gbps link, our protocol only adds 0.38 seconds, or 6.5%, to a 128-token Qwen3-32B RAG pipeline. The released code satisfies directional metric differential privacy (DP) and substantially reduces embedding-inversion and property-inference leakage, demonstrating that a carefully learned shortlist can make private dense retrieval both accurate and practical.

Comments:	33 pages, 9 figures, 18 tables
Subjects:	Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
Cite as:	arXiv:2608.25735 [cs.CR]
	(or arXiv:2608.25735v3 [cs.CR] for this version)
	https://doi.org/10.48550/arXiv.2608.25735 arXiv-issued DOI via DataCite

Submission history

From: Peichun Hua [view email]
[v1] Wed, 26 Aug 2026 12:46:36 UTC (419 KB)
[v2] Sat, 3 Oct 2026 15:53:35 UTC (419 KB)
[v3] Thu, 12 Nov 2026 13:59:25 UTC (425 KB)