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

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Livraison entre le Jeudi 10 septembre 2026 et le Vendredi 11 septembre 2026

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Marque : GENERIC
Vendu par HEAVENBOOKS.MA

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 breac...

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Description produit

Marque
GENERIC
Titre
Privacy and Security for Large Language Models: Hands-On Privacy-Preserving Techniques for Personalized AI
Éditeur
O'Reilly Media
Type de produit
paperback
Présentation du livre
paperback
Date de sortie
2/17/2026 12:00:00 AM
Langue d'origine
English
ISBN
1098160843
Nombre de pages
315 pages
Langue
English
Résumé
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 LLMsLearn secure fine-tuning methodologies for personalizing LLMsUnderstand secure deployment strategies and protection against attacksExplore ethical considerations like bias and transparencyGain insights from real-world case studies across healthcare, finance, and more Read more
Auteur
Baihan Lin
Date de parution
2/17/2026 12:00:00 AM