CoSecRAG: Efficient Vector Similarity Search with Database and Query Privacy for RAG

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Retrieval-augmented generation (RAG) grounds large language models on external knowledge by using vector similarity search over document embeddings to identify relevant documents and supplying their contents as generation context. However, its retrieval stage creates privacy risks: user queries may reveal sensitive intents, while the database may contain proprietary or confidential records. Existing secret-sharing-based solutions that protect both query and database privacy remain costly. To mak