DP2-RAG: An Efficient Full-Process Differential Privacy Implementation in Retrieval-Augmented Generation

Summary

DP2-RAG is a framework that adds end-to-end differential privacy to Retrieval-Augmented Generation, covering both the retrieval stage and the generation stage. It introduces Noise-Aware Retrieval with Correction (NARC), which enforces chunk-level DP with calibrated noise and ranking-bias correction, and the Dual Utility-Exponent Mechanism (DUEM), which provides token-level DP for generated surrogates. Evaluated on six benchmarks, it reduces privacy leakage by over 15% relative to strong baselines while keeping near-baseline Top-k retrieval accuracy.

Topics

• Retrieval-augmented generation

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