3 Advanced concepts of differential privacy for machine learning
This chapter covers
- Design principles of differentially private machine learning algorithms
- Designing and implementing differentially private supervised learning algorithms
- Designing and implementing differentially private unsupervised learning algorithms
- A walk-through of the process of designing and analyzing a differentially private machine learning algorithm
In the previous chapter, we investigated the definition and general usage of differential privacy and the properties of differential privacy that work under different scenarios (e.g., post-processing property, group property, and composition properties). We also looked into common and widely adopted differentially private mechanisms that have served as essential building blocks in various privacy-preserving algorithms and applications. This chapter will walk through how to use those building blocks to design and implement multiple differentially private machine learning algorithms and how to apply such algorithms in real-world scenarios. This will be our second part of differential privacy for machine learning.
We are now at the third chapter of this book. This chapter is the second part of differential privacy in our discussion. This chapter will mainly look at how differential privacy can be implemented in machine learning algorithms by looking at different examples and implementation codes.