Hands-On Differential Privacy: Introduction to the Theory and Practice Using OpenDP, 1st Edition eBook

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Many organizations today analyze and share large, sensitive datasets about individuals. Whether these datasets cover healthcare details, financial records, or exam scores, it’s become more difficult for organizations to protect an individual’s information through deidentification, anonymization, and other traditional statistical disclosure limitation techniques. This practical book explains how differential privacy (DP) can help. 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’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 How to interpret guarantees provided by specific DP data releases

This is a digital product.

Hands-On Differential Privacy: Introduction to the Theory and Practice Using OpenDP 1st Edition is written by Ethan Cowan; Michael Shoemate; Mayana Pereira and published by O’Reilly Media. The Digital and eTextbook ISBNs for Hands-On Differential Privacy are 9781492097709, 1492097705 and the print ISBNs are 9781492097747, 1492097748. Additional ISBNs for this eTextbook include 9781492097716.