Hands-On Differential Privacy: Introduction to the Theory and Practice Using OpenDP
by 作者: Ethan Cowan (Author), Michael Shoemate (Author), Mayana Pereira (Author)
Publisher Finelybook 出版社: O’Reilly Media
Edition 版本: 1st
Publication Date 出版日期: 2024-06-25
Language 语言: English
Pages 页数: 360 pages
ISBN-10 书号: 1492097748
ISBN-13 书号: 9781492097747
Book Description
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
About the Author
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.
