Platform and Model Design for Responsible AI

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The Emergence of Risk-Averse Methodologies and Frameworks

This chapter gives a detailed overview of defining and architecting ML defense frameworks that can protect data, ML models, and other necessary artifacts at different stages of ML training and evaluation pipelines. In this chapter, you will learn about different anonymization, encryption, and application-level privacy techniques, as well as hybrid security measures, that serve as the basis of ML model development for both centralized and distributed learning. In addition, you will also discover scenario-based defense techniques that can be applied to safeguard data and models to solve practical industry-grade ML use cases. The primary objective of this chapter is to explain the application ...