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Pointing the Way, Hiding the Destination: Practical Private Dense Retrieval at Scale

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TL;DR - This paper presents a privacy-preserving dense retrieval protocol for hosted RAG and semantic search that uses learned deep hashing to privately narrow searches before encrypted reranking. It approaches full-corpus retrieval quality while adding only 0.73 seconds to a Qwen3-32B RAG pipeline on a 2.68M-passage corpus.

  • Randomized binary codes produce short candidate lists while hiding queries and reducing full-corpus cryptographic computation.
  • Encrypted reranking and oblivious key transfer protect the exact query, selected result, and access authorization.
  • Shortlists of 200–500 candidates closely match full-corpus retrieval across five zero-shot datasets ranging from 25K to 5.4M documents.
  • The released implementation satisfies directional metric differential privacy and reduces embedding-inversion and property-inference leakage.

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Pointing the Way, Hiding the Destination: Practical Private Dense Retrieval at Scale

arXiv cs.CR Peichun Hua, Danyang Chen, Junan Zhang, Haifeng Sun, Jingyu Wang, Diwen Xue, Mingyu Li, Yunming Xiao 2026-08-26 arXiv:2608.25735
Public signals Semantic Scholar citations 1 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 1 · Influential citations 0 X · N/A Fetched 2026-09-10 14:22:07.494635 UTC

TL;DR - This paper presents a privacy-preserving dense retrieval protocol for hosted RAG and semantic search that uses learned deep hashing to privately narrow searches before encrypted reranking. It approaches full-corpus retrieval quality while adding only 0.73 seconds to a Qwen3-32B RAG pipeline on a 2.68M-passage corpus.

  • Randomized binary codes produce short candidate lists while hiding queries and reducing full-corpus cryptographic computation.
  • Encrypted reranking and oblivious key transfer protect the exact query, selected result, and access authorization.
  • Shortlists of 200–500 candidates closely match full-corpus retrieval across five zero-shot datasets ranging from 25K to 5.4M documents.
  • The released implementation satisfies directional metric differential privacy and reduces embedding-inversion and property-inference leakage.
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