Privacy and Security for Large Language Models Hands-On Privacy-Preserving Techniques for Personalized AI | Baihan Lin | Shroff/O'Reilly

📘 Book Name: Privacy and Security for Large Language Models: Hands-On Privacy-Preserving Techniques for Personalized AI 🛡️

✍️ Author: Baihan Lin

🏢 Publisher: Shroff Publishers / O'Reilly Media

📅 Edition: 2026 Indian Edition ✨

🆔 ISBN: 9789368083924

Privacy and Security for Large Language Models is a critical resource for AI researchers, privacy engineers, and developers dedicated to building personalized AI without compromising user data 🚀. As LLMs become more integrated into our daily lives—processing everything from medical records to private chats—the risk of data leakage grows. Baihan Lin provides a hands-on guide to implementing cutting-edge privacy-preserving machine learning (PPML) techniques to secure the next generation of AI 🧠.

✨ Key Features:

• ✅ Differential Privacy (DP): Learn how to add mathematical noise to training data to prevent the extraction of individual secrets by the model 💡.

• ✅ Federated Learning: Explore decentralized training methods where the model moves to the data, rather than the data moving to a central server 📊.

• ✅ Trusted Execution Environments (TEEs): Practical implementation of hardware-based security to run model inference in isolated "enclaves" 🛡️.

• ✅ Attack Defense: Detailed analysis of Prompt Injection, Membership Inference Attacks (MIA), and Data Poisoning, along with robust defense strategies 📈.

• ✅ Homomorphic Encryption: Understand how to perform computations on encrypted data, keeping the actual values hidden even from the AI provider 🏆.

• ✅ Compliance & Ethics: Guidance on aligning AI development with global privacy regulations like GDPR and CCPA 📝.