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

Master the essential techniques for protecting user data and securing AI systems with this comprehensive guide to privacy-preserving methods in large language models. Written by Baihan Lin, this hands-on book explores practical approaches to implementing privacy safeguards, differential privacy, federated learning, and secure computation in modern AI applications. Perfect for machine learning engineers, data scientists, and AI practitioners who need to build personalized systems while maintaining robust security standards. Learn how to balance model performance with privacy requirements and deploy AI solutions that respect user confidentiality. An indispensable resource for anyone developing responsible, privacy-conscious AI systems.