This chapter covers:
- Introducing the design principles of differentially private machine learning algorithms
- Designing and implementing differentially private supervised learning algorithms (e.g., Naive Bayes classification, logistic regression and linear regression)
- Designing and implementing differentially private unsupervised learning algorithms (e.g., K-means clustering)
- Working with a case study on “Differentially Private Principal Component Analysis” to walk you through the process of designing and analyzing a differentially private machine learning algorithm
In the previous chapter, we have looked into the definition and general usage of differential privacy, the properties of differential privacy that works under different scenarios (e.g., post-processing property, group property and composition properties), and widely adopted differential privacy mechanisms in use today, that have served as the most important building blocks in various privacy-preserving algorithms and applications. In this chapter, we will walk through how to use those building blocks to design and implement various differentially private machine learning algorithms, and how to apply such algorithms in real-world scenarios. This chapter covers the 2nd part of differential privacy for machine learning.
