Quantization Enables Private Dense Retrieval against Malicious Service Providers
Dense retrieval, the key component of Retrieval Augmented Generation (RAG), retrieves the most relevant documents by comparing dense vector representations of queries and passages from a large corpus. In privacy-sensitive applications, the server observes the query and controls which evidence is returned, creating both confidentiality and integrity risks. We formulate private dense retrieval as providing query privacy and retrieval integrity against a malicious server, and develop a two-round cryptographic protocol that provides both guarantees. Our protocol reduces private and verifiable retrieval to multiplication of a committed matrix by an encrypted vector and uses low-bit quantizatio...
| Authors | Louis Tremblay Thibault, Sofiane Azogagh, Marc-Olivier Killijian et al. |
|---|---|
| Primary category | cs.CR |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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