Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 15·5 min read
Privacy-Preserving RAG by Concealing Sensitive Information from External LLMs
TL;DR
A new framework called the Sensitive Entity Alias Generator (SEAG) enables users to interact with external Large Language Models (LLMs) while concealing sensitive information. SEAG achieves over 80% accuracy in providing correct responses without exposing confidential data.
✦ Why It Matters
Implement SEAG to protect sensitive information when using external AI models in your applications.
Key Takeaways
- Use SEAG to mask sensitive data in queries.
- Train SEAG with custom datasets for better performance.
- Evaluate model accuracy to ensure privacy compliance.
- Integrate SEAG with existing AI applications for enhanced security.
How It Works
SEAG identifies sensitive entities in user queries and retrieved documents, generates aliases for these entities, and constructs an entity replacement table. This table is then used to replace sensitive terms before the data is sent to external LLMs, ensuring that confidential information remains concealed.