Privacy and Security for Large Language Models (Early Release)

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. This book serves as a much-needed guide to addressing these pressing concerns. Dr. Baihan Lin offers a comprehensive exploration of privacy-preserving and security techniques like differential privacy, federated learning, and homomorphic encryption, applied specifically to LLMs.

This book serves as a much-needed guide to addressing these pressing concerns. Dr. Baihan Lin 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, this book is a vital resource for developing secure, ethical, and personalized AI solutions in today's privacy-conscious landscape. By reading this book, you will

Discover privacy-preserving techniques for LLMs
Learn secure fine-tuning methodologies for personalizing LLMs
Understand secure deployment strategies and protection against attacks
Explore ethical considerations like bias and transparency
Gain insights from real-world case studies across healthcare, finance, and more
Examine the legal and cultural landscape of AI deployment