Ethan Cowan, Michael Shoemate, Mayana Pereira

Hands-On Differential Privacy (eBook, PDF)

• Format: PDF

Produktbeschreibung

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'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

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Produktdetails

• Verlag: O'Reilly Media
• Seitenzahl: 362
• Erscheinungstermin: 16. Mai 2024
• Englisch
• ISBN-13: 9781492097716
• Artikelnr.: 72465643

Autorenporträt

Ethan Cowan worked on software and research topics as part of the OpenDP team from 2020 to 2022. In particular, he focused on privatizing machine learning models and developing platforms for analyzing sensitive data with built-in differential privacy. Ethan now studies the history and ethics of emerging technology.