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Hands-on differential privacy - Ethan Cowan, Michael Shoemate, Mayana Pereira

Hands-on differential privacy

introduction to the theory and practice using openDP
Buch | Softcover
275 Seiten
2024 | 1. Auflage
O'Reilly Media (Verlag)
978-1-4920-9774-7 (ISBN)
CHF 109,95 inkl. MwSt
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. This practical book explains how differential privacy (DP) can help.
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 and 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

Ethan Cowan works on software and research topics as part of the Open Differential Privacy (OpenDP) team at Harvard. In particular, he focuses on privatizing machine learning models and developing platforms for analyzing sensitive data with built-in differential privacy. Ethan also works at the intersection of ethics, fairness, and federated learning. Michael Shoemate works for the research organization TwoRavens, developing tools for visualizing data and conducting statistical analysis. His work has been spread over several different projects: the core project, metadata service, and EventData. He's also built a collection of reusable modular UI components he's named "common" for rapid and homogenous frontend development in Mithril. Mayana Pereira works on applying machine learning and privacy-preserving techniques to a diverse range of practical problems at Microsoft's AI for Good Team. Mayana is also an active collaborator of OpenDP, an open-source project for the differential privacy community to develop general-purpose, vetted, usable, and scalable tools for differential privacy.

Erscheinungsdatum
Reihe/Serie Animals
Zusatzinfo Illustrationen
Verlagsort Sebastopol
Sprache englisch
Maße 178 x 233 mm
Themenwelt Mathematik / Informatik Informatik Datenbanken
Informatik Grafik / Design Digitale Bildverarbeitung
Informatik Netzwerke Sicherheit / Firewall
Informatik Software Entwicklung User Interfaces (HCI)
Mathematik / Informatik Informatik Theorie / Studium
ISBN-10 1-4920-9774-8 / 1492097748
ISBN-13 978-1-4920-9774-7 / 9781492097747
Zustand Neuware
Informationen gemäß Produktsicherheitsverordnung (GPSR)
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