Privacy-Preserving Personalization Frameworks for Large Language Models

Abstract

This article explores innovative approaches to personalizing Large Language Models while maintaining robust privacy protections for users. It examines federated learning architectures, voice biometric authentication, differential privacy techniques, and hierarchical user profiling methods. The investigation delves into developing adaptive frameworks that can securely handle multiple users while preserving individual privacy through advanced encryption and secure processing environments. This article presents a comprehensive analysis of emerging technologies and methodologies that enable personalized AI experiences without compromising user data security. By addressing the critical balance between customization and privacy, this article contributes to the evolving landscape of secure, user-centric AI systems, offering practical solutions for implementing privacy-preserving personalization in modern language models.