Privacy-Aware Decoding: Mitigating Privacy Leakage of Large Language Models in Retrieval-Augmented Generation
arXiv:2508.03098v2 Announce Type: replace Abstract: 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 confidence-based screening to selec
The increasing deployment of LLMs in enterprise and sensitive applications makes data privacy a critical and immediate concern, driving research into mitigation techniques.
Ensuring data privacy in RAG systems is crucial for their adoption in regulated sectors and for maintaining user trust, directly impacting the commercial viability and ethical deployment of advanced AI applications.
The proposed Privacy-Aware Decoding offers a new, lightweight method to enhance privacy in RAG systems, potentially reducing the risk of sensitive data leakage and expanding the safe application of LLMs.
- · Enterprises using RAG with sensitive data
- · AI-as-a-service providers
- · Users of RAG-powered applications
- · Privacy-focused AI research
- · Attackers attempting data extraction from RAG systems
Increased trust and wider adoption of Retrieval-Augmented Generation (RAG) systems in privacy-sensitive domains.
Reduced regulatory hurdles for deploying LLM-based solutions in industries like healthcare and finance, fostering innovation.
The development of a new 'privacy layer' in AI inference chips or architectures as privacy-aware decoding becomes a standard feature.
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