Privacy and Security for Large Language Models: Hands-On Privacy-Preserving Techniques for Personalized AI

Baihan Lin

As the deployment of AI technologies surges, the need to safeguard privacy and security in the use of large language models (LLMs) is more crucial than ever. Professionals face the challenge of leveraging the immense power of LLMs for personalized applications while ensuring stringent data privacy and security. The stakes are high, as privacy breaches and data leaks can lead to significant reputational and financial repercussions.

This book serves as a much-needed guide to addressing these pressing concerns. It offers a comprehensive exploration of privacy-preserving and security techniques like differential privacy, federated learning, and homomorphic encryption, applied specifically to LLMs. With its hands-on code examples, real-world case studies, and robust fine-tuning methodologies in domain-specific applications, the book is a vital resource for developing secure, ethical, and personalized AI solutions in today’s privacy-conscious landscape.