Learning to Erase Private Knowledge from Multi-Documents for Retrieval-Augmented Large Language Models
arXiv:2504.09910v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) is a promising technique for applying LLMs to proprietary domains. However, retrieved documents may contain sensitive knowledge, posing risks of privacy leakage in generative results. Thus, effectively erasing private information from retrieved documents is a key challenge for RAG. Unlike traditional text anonymization, RAG should consider: (1) the inherent multi-document reasoning may face de-anonymization attacks; (2) private knowledge varies by scenarios, so users should be allowed to customize which in
The increasing adoption of RAG in enterprise and sensitive domains necessitates advanced methods for data privacy and security, as current anonymization techniques are insufficient for complex multi-document reasoning.
This research addresses a critical vulnerability in RAG systems, enabling safer and more ethical deployment of large language models in industries handling private or proprietary information.
The ability to customize and erase private knowledge from RAG documents changes how organizations can integrate LLMs with sensitive data, mitigating risks of privacy leakage and de-anonymization attacks.
- · Enterprise AI Adopters
- · Cybersecurity Firms
- · Healthcare
- · Financial Services
- · Organizations with poor data governance
- · Traditional anonymization solutions
Increased trust and accelerated adoption of RAG-based LLMs in highly regulated industries by addressing privacy concerns.
Development of new regulatory standards and compliance frameworks specifically for privacy-preserving RAG systems.
The integration of such privacy-preserving techniques could become a competitive differentiator for AI solutions providers, leading to a new 'privacy-first AI' market segment.
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