Privacy and Security for Large Language Models Hands-On Privacy-Preserving Techniques for Personalized AI | 3.78 MB
Title: Privacy and Security for Large Language Models (for . .)
Author: Baihan Lan
Category: Nonfiction, Computers, Advanced Computing, Natural Language Processing, Internet, Security, Business & Finance, Industries & Professions, Industries
Language: English | 318 Pages | ISBN: 9781098160814
Description:
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. 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.
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. 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.
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