Baihan Lin | Privacy and Security for Large Language Models. Hands-On Privacy-Preserving Techniques for Personalized AI. 1st Edition (2026) [PDF, EPUB]
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.
Читать фрагмент
Дополнительная информация:
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
Аннотация
Baihan Lin | Privacy and Security for Large Language Models. Hands-On Privacy-Preserving Techniques for Personalized AI. 1st Edition (2026) [PDF, EPUB]
picАвтор: Baihan Lin
Издательство: O'Reilly Media
ISBN: 978-1098160845
Жанр: Natural Language Processing, Privacy & Online Safety, Computer Network Security
Язык: Английский
Формат: PDF, EPUB
Качество: Изначально электронное (ebook)
Иллюстрации: Цветные и черно-белые
Описание:
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.
Читать фрагмент
Дополнительная информация:
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