# Privacy Challenges and Solutions in Retrieval-Augmented Generation-Enhanced LLMs for Healthcare Chatbots: A Review of Applications, Risks, and Future Directions

arXiv: https://arxiv.org/abs/2511.11347 · HTML: https://mlsanity.com/paper/2511.11347/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 19 of its 74 references that are in the map.
- First posted on arXiv 14 Nov 2025.

## Placed in

- Retrieval-augmented generation / [Privacy and security of RAG](https://mlsanity.com/rag/privacy-and-security-of-rag/)

## Builds on (most cited first, 19 of 19)

- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)](https://mlsanity.com/paper/2402.16893/) (2024)
- [Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2402.17840/) (2024)
- [Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data](https://mlsanity.com/paper/2406.14773/) (2024)
- [The Ethics of ChatGPT in Medicine and Healthcare: A Systematic Review on Large Language Models (LLMs)](https://mlsanity.com/paper/2403.14473/) (2024)
- [Is My Data in Your Retrieval Database? Membership Inference Attacks Against Retrieval Augmented Generation](https://mlsanity.com/paper/2405.20446/) (2024)
- [Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy](https://mlsanity.com/paper/2412.04697/) (2024)
- [Mask-based Membership Inference Attacks for Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.20142/) (2024)
- [Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries](https://mlsanity.com/paper/2406.10280/) (2024)
- [Generating Is Believing: Membership Inference Attacks against Retrieval-Augmented Generation](https://mlsanity.com/paper/2406.19234/) (2024)
- [Data Extraction Attacks in Retrieval-Augmented Generation via Backdoors](https://mlsanity.com/paper/2411.01705/) (2024)
- [A Survey on Privacy Risks and Protection in Large Language Models](https://mlsanity.com/paper/2505.01976/) (2025)
- [Silent Leaks: Implicit Knowledge Extraction Attack on RAG Systems through Benign Queries](https://mlsanity.com/paper/2505.15420/) (2025)
- [RAG with Differential Privacy](https://mlsanity.com/paper/2412.19291/) (2024)
- [Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.19101/) (2025)
- [Privacy-Aware Decoding: Mitigating Privacy Leakage of Large Language Models in Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.03098/) (2025)
- [HyFedRAG: A Federated Retrieval-Augmented Generation Framework for Heterogeneous and Privacy-Sensitive Data](https://mlsanity.com/paper/2509.06444/) (2025)
- [Privacy-Preserving Reasoning with Knowledge-Distilled Parametric Retrieval Augmented Generation](https://mlsanity.com/paper/2509.01088/) (2025)
- [Privacy-protected Retrieval-Augmented Generation for Knowledge Graph Question Answering](https://mlsanity.com/paper/2508.08785/) (2025)

## Built on by (most cited first, 3 of 3)

- [When RAG Chatbots Expose Their Backend: An Anonymized Case Study of Privacy and Security Risks in Patient-Facing Medical AI](https://mlsanity.com/paper/2605.00796/) (2026)
- [Is External Database Protection Static in Retrieval-Augmented Generation? Rethinking Privacy Preservation under Dynamic Queries](https://mlsanity.com/paper/2607.14811/) (2026)
- [RAG-PIBench: A Leakage-Aware Benchmark for Prompt-Injection Detection in Trustworthy RAG Systems](https://mlsanity.com/paper/2610.08571/) (2026)

## Abstract (arXiv)

Retrieval-augmented generation (RAG) has rapidly emerged as a transformative approach for integrating large language models into clinical and biomedical workflows. However, privacy risks, such as protected health information (PHI) exposure, remain inconsistently mitigated. This review provides a thorough analysis of the current landscape of RAG applications in healthcare, including (i) sensitive data type across clinical scenarios, (ii) the associated privacy risks, (iii) current and emerging data-privacy protection mechanisms and (iv) future direction for patient data privacy protection. We synthesize 23 articles on RAG applications in healthcare and systematically analyze privacy challenges through a pipeline-structured framework encompassing data storage, transmission, retrieval and generation stages, delineating potential failure modes, their underlying causes in threat models and system mechanisms, and their practical implications. Building on this analysis, we critically review 17 articles on privacy-preserving strategies for RAG systems. Our evaluation reveals critical gaps, including insufficient clinical validation, absence of standardized evaluation frameworks, and lack of automated assessment tools. We propose actionable directions based on these limitations and conclude with a call to action. This review provides researchers and practitioners with a structured framework for understanding privacy vulnerabilities in healthcare RAG and offers a roadmap toward developing systems that achieve both clinical effectiveness and robust privacy preservation.