Privacy-enhancing technologies

Privacy-enhancing technologies (PETs) are a set of technologies and techniques that help protect sensitive information while still allowing useful analysis and processing of the data. Here is a high-level introduction to some of the commonly used PETs.

Differential privacy

This is a technique that adds a certain amount of noise to a dataset to protect the privacy of individual records while still allowing for statistical analysis. Differential privacy ensures that any queries made on a dataset do not reveal information about specific individuals, making it a powerful tool for protecting privacy in large datasets. We will go through differential privacy in this chapter and the rest of the PETs in other, subsequent chapters.

Federated learning

This is a technique for training machine learning models on data that is distributed across multiple devices or servers, without the need to centralize the data. In federated learning, the model is trained...