Summary

In summary, in this chapter, we went through open source frameworks including PyDP, PipelineDP, Tumult Analytics, and PySpark in order to implement differential privacy. We implemented fraud detection machine learning models with and without differential privacy by developing a private stochastic gradient descent algorithm. We also implemented deep learning models and trained the models with differential privacy using the Opacus framework, which is based on PyTorch. Finally, we covered the limitations of differential privacy and strategies to overcome these limitations.

In the next chapter, we’ll learn about the need for federated learning as we deep dive into it, covering the algorithms used and the frameworks that support federated learning, and explore an end-to-end implementation of a fraud detection use case using federated learning.