Summary of Trustworthy Ai: Securing Sensitive Data in Large Language Models, by Georgios Feretzakis and Vassilios S. Verykios
Trustworthy AI: Securing Sensitive Data in Large Language Models
by Georgios Feretzakis, Vassilios S. Verykios
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a comprehensive framework for embedding trust mechanisms into Large Language Models (LLMs) to dynamically control the disclosure of sensitive information. The framework integrates three core components: User Trust Profiling, Information Sensitivity Detection, and Adaptive Output Control. By leveraging techniques such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), Named Entity Recognition (NER), contextual analysis, and privacy-preserving methods like differential privacy, the system ensures that sensitive information is disclosed appropriately based on the user’s trust level. The framework aims to balance data utility and privacy, offering a novel approach to securely deploying LLMs in high-risk environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a way to make sure Large Language Models don’t share personal or private information without permission. It does this by making three main parts work together: User Trust Profiling, Information Sensitivity Detection, and Adaptive Output Control. These parts use techniques like Role-Based Access Control and Named Entity Recognition to decide what sensitive information to share based on who is asking for it. The goal is to keep both the data safe and useful. |
Keywords
» Artificial intelligence » Embedding » Named entity recognition » Ner
