E-Books →Privacy and Security for Large Language Models (Early Release)

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