Hands–On Differential Privacy Introduction to the Theory and Practice Using OpenDP

Authors Ethan Cowan, Michael Shoemate, 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'tWhat preserving individual privacy in a dataset entailsHow to apply DP in several real-world scenarios and datasetsPotential privacy attack methods, including what it means to perform a reidentification attackHow to use the OpenDP library in privacy-preserving data releasesHow to interpret guarantees provided by specific DP data releases