Trustworthy AI: Securing Sensitive Data in Large Language Models
It addresses privacy concerns for users in high-risk environments, but the approach appears incremental as it combines existing techniques like RBAC and differential privacy.
This paper tackles the problem of privacy and data security in Large Language Models (LLMs) deployed in sensitive domains like healthcare and finance by proposing a framework that integrates trust mechanisms to dynamically control sensitive information disclosure, aiming to balance data utility and privacy.
Large Language Models (LLMs) have transformed natural language processing (NLP) by enabling robust text generation and understanding. However, their deployment in sensitive domains like healthcare, finance, and legal services raises critical concerns about privacy and data security. This paper proposes a comprehensive framework for embedding trust mechanisms into 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. By focusing on balancing data utility and privacy, the proposed solution offers a novel approach to securely deploying LLMs in high-risk environments. Future work will focus on testing this framework across various domains to evaluate its effectiveness in managing sensitive data while maintaining system efficiency.