Security and Privacy in Machine Learning
In the digital world that we live in, preserving the privacy of users’ data and their personal information, as well as ensuring the security of their digital information and assets, are of great importance in technology development. This is not an exception for technologies built on top of machine learning models. We briefly talked about this topic in Chapter 3, Debugging toward Responsible AI. In this chapter, we will provide you with more details to help you start your journey in learning more about privacy preservation and ensuring security in developing machine learning models and technologies.
In this chapter, we will cover the following topics:
- Encryption techniques and their use in machine learning
- Homomorphic encryption
- Differential privacy
- Federated learning
By the end of this chapter, you will understand the challenges in preserving privacy and ensuring security in machine learning settings, and...
