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Abstract

Dense vector embeddings underpin modern semantic search and Retrieval-Augmented Generation (RAG), yet a growing body of work shows that an embedding can be inverted back into the text that produced it with alarming fidelity: once a vector database leaks, the documents behind it leak with it. The two textbook defences lie at opposite extremes—encrypting the entire search with fully homomorphic encryption is cryptographically sound but far too slow for corpora of millions of documents, whereas injecting privacy-preserving noise degrades ranking quality long before it provides meaningful protection.