Privacy and Security for Large Language Models

Chapter 4. Privacy-Preserving Training Techniques

In our journey so far, you have learned how to create LLMs and how to evaluate them properly in terms of their health conditions in privacy and security. Now you are going to learn how to keep these AI friends healthy by building these protections directly into your models. In this chapter, you’re going to explore a class of techniques that allow your AI to train on sensitive information while keeping that information under wraps.

Privacy-preserving methods represent a critical frontier in AI development, especially as LLMs increasingly process personal, medical, financial, and other sensitive information. These approaches enable models to extract valuable patterns and insights from data without compromising the confidentiality of individual records or examples. They function by creating mathematical guarantees and cryptographic protections that limit what information can be extracted or inferred from the trained model.

In this chapter, you’ll explore several key techniques that allow AI systems to learn from sensitive information while maintaining strong privacy protections. These methods represent the intersection of machine learning, cryptography, and privacy theory, creating systems that can analyze data they cannot fully “see” in its original form.

We’ll cover five major classes of privacy-preserving techniques: differential privacy, federated learning, homomorphic encryption, multi-party computation, and privacy-preserving ...