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:
| ISBN: | 9781098160845 |
| Publication date: | 30th January 2026 |
| Author: | Baihan Lin |
| Publisher: | O'Reilly Media |
| Format: | Paperback |
| Pagination: | 315 pages |
| Genres: |
Digital and information technologies: Health and safety aspects Digital Lifestyle and online world: consumer and user guides Computer security Natural language and machine translation |
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:
Privacy and Security for Large Language Models features in the following genres: Digital and information technologies: Health and safety aspects, Digital Lifestyle and online world: consumer and user guides, Computer security, Natural language and machine translation
Privacy and Security for Large Language Models is available in Paperback
Privacy and Security for Large Language Models was written by Baihan Lin and published by O'Reilly Media
Privacy and Security for Large Language Models has 315 pages
£57.59