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by Baihan Lin (Author)
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
| Shipping | This item ships to |
| Delivery | Estimated between and . Will usually ship within 1 business day. |
Description
by Baihan Lin (Author)
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
Shipping
| Shipping | This item ships to |
| Delivery | Estimated between and . Will usually ship within 1 business day. |
