Authors Ethan Cowan and Mayana Pereira explain how these techniques enable data scientists, researchers, and programmers to run statistical analyses that hide the contribution of any single individual. You'll dive into basic DP concepts and understand how to use open source tools to create differentially private statistics, explore how to assess the utility/privacy trade-offs, and learn how to integrate differential privacy into workflows.
With this book, you'll learn
How DP guarantees privacy when other data anonymization methods don't
What preserving individual privacy in a dataset entails
How to apply DP in several real-world scenarios and datasets
Potential privacy attack methods, including what it means to perform a reidentification attack
How to use the OpenDP library in privacy-preserving data releases