9 Compressive privacy for machine learning
This chapter covers
- Understanding compressive privacy
- Introducing compressive privacy for machine learning applications
- Implementing compressive privacy from theory to practice
- A compressive privacy solution for privacy-preserving machine learning
In the previous chapters, we looked into the concept of differential privacy, local differential privacy, privacy-preserving synthetic data generation, privacy-preserving data mining, and their applications in designing privacy-preserving machine learning solutions. In a quick recap, in differential privacy, a trusted data curator collects data from individuals and produces differentially private results by adding precisely computed noise to the aggregation of individuals’ data. In local differential privacy, individuals send their data to the data aggregator after privatizing data by perturbation, which eliminates the need for a trusted data curator to collect the private data from individuals. Privacy-preserving synthetic data generation provides a promising solution for private data sharing, which generates synthetic yet representative data that can be used to share among multiple parties safely and securely.