Hands-On Differential Privacy

Preface

In this book, you will learn the mathematically rigorous definition of privacy known as differential privacy (DP). Differential privacy can be used to accurately release statistical information about a data set that does not reveal information about specific individuals in the data set. Such an analysis leads to the publication of information about the data set, known as a DP data release. This book shows you how to design data analysis workflows for sensitive data sets in a way that guarantees privacy.

DP is the preferred and trustworthy solution for data privatization needs:

• DP guarantees are robust against adversaries with unbounded resources, like auxiliary data and unlimited computational power.

• DP guarantees are interpretable in terms of the risk of individuals in the data.

• DP guarantees degrade gracefully as more data releases are made.

Data privacy is a vast topic. If you’ve previously studied data privacy, you might have learned about securing databases from hacking or creating cryptographic hashes. You may have also studied virtual private networks (VPNs) and other tools to prevent tracking online. These concepts are focused on guaranteeing privacy by not revealing anything about the data. However, the notion of privacy addressed in this book relates to privacy-preserving data releases. The goal of a privacy-preserving data release is to release information about a data set without revealing information about specific individuals in the data. Differential ...