Название: Privacy and Security for Large Language Models: Hands-On Privacy-Preserving Techniques for Personalized AI (Early Release)
Автор: Baihan Lin
Издательство: O’Reilly Media, Inc.
Год: 2024-11-20
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

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.

Welcome to the fascinating world of large language models (LLMs), where AI meets human-like language, and the possibilities are limited only by our imagination (and perhaps a few thousand GPUs). In this rapidly evolving landscape, LLMs have emerged as the vanguards of natural language processing, computer vision and real-world multi-modal applications such as robotics and video generation, ready to tackle a myriad of tasks with their impressive intellect and adaptability. But hold on to your data, because with great power comes great responsibility, and these models are not without their challenges in the realms of privacy, security, and ethics. In this book, we’ll embark on an exciting journey to explore the fascinating terrain of LLMs in personalized AI, equipping you with the tools and knowledge to harness their potential while navigating the complexities that come with them.

Whether you’re a developer looking to build privacy-preserving AI applications, a researcher seeking to advance the frontiers of LLM technology, or a decision-maker grappling with the ethical and societal implications of these systems, this book has something to offer. We’ll dive deep into the technical aspects of LLMs, from their architectures and training techniques to the latest advances in privacy-preserving machine learning. At the same time, we’ll step back and consider the broader cultural, social, and legal landscapes that shape the development and deployment of these technologies.

By reading this book, you will:

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
Examine the legal and cultural landscape of AI deployment






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