RAG with Differential Privacy

Retrieval-Augmented Generation (RAG, (Lewis et al. 2021)) has become a popular approach to enhance the capabilities of Large Language Models (LLMs) by supplying them with up-to-date and pertinent information. This method is particularly valuable in environments where knowledge bases are large and rapidly evolving, such as news websites, social media platforms, or scientific research databases. By integrating fresh context, RAG helps mitigate the risk of "hallucinations"--instances where the model generates plausible but factually incorrect information--and significantly improves the overall quality and relevance of the responses generated by the LLM. However, incorporating external documents into the generation process introduces substantial privacy concerns. When these documents are included in the input prompt for the LLM, there is no foolproof way to ensure that the generated response will not accidentally reveal sensitive or confidential data (Qi et al. 2024). This potential for inadvertent data exposure can lead to serious breaches of privacy and presents significant ethical challenges. For instance, if an LLM is used in a healthcare setting and it accidentally includes patient information from an external document in its response, it could violate patient confidentiality and legal regulations. This paper describes a practical solution (DP-RAG) aimed at addressing these privacy concerns with Differential Privacy (DP).