Random projection is a simple, practical alternative to differential privacy for protecting RAG datastores: it maintains retrieval quality while defending against data extraction attacks at lower computational cost.
This paper proposes using random projection to protect sensitive data in RAG systems. By applying the same random transformation to both stored knowledge embeddings and user queries, the method preserves search accuracy while preventing attackers from extracting private information from the datastore—without the computational overhead of formal privacy guarantees.