Privacy and Security for Large Language Models by Baihan Lin

Privacy and Security for Large Language Models by Baihan Lin

As AI becomes embedded in every corner of business and life, one question becomes critical: how do we keep it safe? Privacy and Security for Large Language Models by Baihan Lin is the definitive hands-on guide to building AI systems that protect user data, resist attacks, and comply with the growing landscape of AI regulations — without sacrificing performance.

This O'Reilly book goes beyond theory. You'll explore privacy-preserving techniques like differential privacy, federated learning, and secure computation — all applied directly to LLMs. Whether you're a machine learning engineer, security professional, or AI product leader, Lin gives you the technical depth and practical frameworks to build AI you can actually trust.

In a world where data breaches and AI misuse make headlines daily, this book is not optional reading — it's essential.

? ???? ?? ??????: LLM security fundamentals · Differential privacy · Federated learning · Adversarial attack defense · AI compliance & governance

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