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Privacy and Security for Large Language Models by Baihan Lin

Privacy and Security for Large Language Models by Baihan Lin

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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.

✅ আপনি যা শিখবেন: LLM security fundamentals · Differential privacy · Federated learning · Adversarial attack defense · AI compliance & governance

📚 বইটির বিশেষত্ব

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📚 Premium Quality Books — Printed on 80 GSM Cornofuly paper, eye-friendly pages, and stitched binding for durability.
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