Privacy and Security for Large Language Models

Regular price €76.99
Quantity:
In stock with our UK publisher. 14-28 days
Delivery/Collection within 10-20 working days
14 days return policy Shipping & Delivery
A01=Baihan Lin
Author_Baihan Lin
Category=UDD
Category=UR
Category=UYQL
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_new_release
eq_nobargain
eq_non-fiction
Privacy-Preserving Techniques Large Language Models (LLMs) Fine-Tuning Transfer Learning Personalization Domain-Specific Fine-Tuning Multi-Party Computation (MPC) Federated Learning Differential Privacy Homomorphic Encryption Data Security Adversarial Att
Privacy-Preserving Techniques Large Language Models LLMs Fine-Tuning Transfer Learning Personalization Domain-Specific Fine-Tuning Multi-Party Computation MPC Federated Learning Differential Privacy Homomorphic Encryption Data Security Adversarial Attacks

Product details

  • ISBN 9781098160845
  • Dimensions: 178 x 232mm
  • Publication Date: 30 Jan 2026
  • Publisher: O'Reilly Media
  • Publication City/Country: US
  • Product Form: Paperback
Secure checkout Fast Shipping Easy returns

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. 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'll:

  • 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
Dr. Baihan Lin is a leading computer scientist, neuroscientist, inventor, and professor specializing in speech and natural language processing (NLP). He holds faculty positions at Harvard University and the Icahn School of Medicine at Mount Sinai.

More from this author