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Total Size:
4.5 MB
Info Hash:
B2E3B59B09BABC0550D012723C4A74EE543D982E
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Added:
Jan. 14, 2026, 11:49 a.m.
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(Last updated: Jan. 14, 2026, 11:50 a.m.)
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| Lin B. Privacy and Security for Large Language Models. Hands-On...AI 2026.pdf | 4.5 MB |
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NOTE
SOURCE: Lin B. Privacy and Security for Large Language Models. Hands-On...AI 2026
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COVER
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MEDIAINFO
Textbook in PDF format 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 Examine the legal and cultural landscape of AI deployment Who Should Read This Book: This book is written for AI practitioners, data scientists, machine learning engineers, and security professionals who find themselves at the forefront of deploying LLMs in real-world environments. You likely already understand the basics of machine learning and have worked with neural networks, but you’re now confronting questions that go beyond model performance. How do you fine-tune a model on sensitive medical data without exposing patient information? How do you deploy personalized AI systems while maintaining user privacy? How do you defend against adversarial attacks that didn’t exist just a few years ago? You might be a machine learning engineer at a healthcare startup, wondering how to build HIPAA-compliant AI systems. Perhaps you’re a data scientist at a financial institution, tasked with creating personalized recommendation systems that must comply with strict privacy regulations. Or you could be a security researcher, investigating new attack vectors that emerge when AI systems process human language at scale. I assume you have intermediate to advanced expertise in Machine Learning, familiarity with Python programming, and a working knowledge of deep learning frameworks. More importantly, I assume you’re grappling with the practical challenges of responsible AI deployment, the challenges that textbooks often gloss over but that practitioners face every day. 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
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