Privacy-Aware Decoding: Mitigating Privacy Leakage of Large Language Models in Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) enhances the factual accuracy of large language models (LLMs) by conditioning outputs on external knowledge sources. However, when retrieval involves private or sensitive data, RAG systems are susceptible to extraction attacks that can leak confidential information through generated responses. We propose Privacy-Aware Decoding (PAD), a lightweight, inference-time defense that adaptively injects calibrated Gaussian noise into token logits during generation. PAD integrates logit clipping, confidence-based screening to selectively protect high-risk tokens, a risk-adaptive calibration scale that allocates noise where it is most needed, and context-aware noise calibration to balance privacy with generation quality. A Rényi Differential Privacy (RDP) accountant tracks cumulative privacy loss, providing explicit per-response (𝜀, 𝛿) privacy accounting over the protected tokens under the realized noise schedule. Unlike prior approaches requiring retraining or corpus-level filtering, PAD is model-agnostic and operates entirely at decoding time with minimal computational overhead. Experiments on three real-world datasets demonstrate that PAD substantially reduces private information leakage while preserving response utility, outperforming existing retrieval-and post-processing-based defenses. Our work takes an important step toward mitigating privacy risks in RAG via decoding strategies, paving the way for universal and scalable privacy solutions in sensitive domains. Our code is available: https://github.com/wang2226/PAD

• Security and privacy → Data anonymization and sanitization; • Computing methodologies → Natural language generation.