Ensuring Data Security in Large Language Models through Trustworthy AI
Author(s): Dr. Priya Nair¹, Dr. Ramesh Babu², and Dr. Anjali Menon³
Affiliation: � Department of Computer Science � Department of Electrical Engineering � Department of Information Technology 1,2,3SRM Institute of Science and Technology, Chennai, India
Page No: 31-43-
Volume issue & Publishing Year: Volume 1 Issue 7 ,Dec-2024
Journal: International Journal of Modern Engineering and Management | IJMEM
ISSN NO: 3048-8230
DOI:
Abstract:
Large language models (LLMs) have revolutionized Natural Language Processing (NLP) by enabling advanced capabilities in text generation and comprehension. However, their use in sensitive sectors such as healthcare, finance, and legal services raises significant concerns regarding privacy and data security. This paper introduces a comprehensive framework designed to integrate trust mechanisms into LLMs to regulate the disclosure of sensitive data. The framework comprises three key components: User Trust Profiling, Information Sensitivity Detection, and Adaptive Output Control. By incorporating methods like Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), Named Entity Recognition (NER), contextual analysis, and privacy-preserving techniques such as differential privacy, the system ensures that sensitive information is shared appropriately according to the user's trust level. The proposed solution strikes a balance between maintaining data utility and safeguarding privacy, offering a novel approach for the secure application of LLMs in high-risk environments. Future research will focus on testing the framework in various domains to assess its effectiveness in protecting sensitive data while ensuring system efficiency.
Keywords:
large language models; trust mechanisms; sensitive data; role-based access control; attribute-based access control; data privacy; privacy-preserving techniques; named entity recognition; differential privacy; AI ethics.
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