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

by Ethan Cowan, Michael Shoemate, Mayana Pereira

Overview

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

Product Details

ISBN-13:	9781492097747
Publisher:	O'Reilly Media, Incorporated
Publication date:	06/25/2024
Pages:	360
Product dimensions:	7.00(w) x 9.19(h) x 0.00(d)

About the Author

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.

Michael Shoemate is the architect for the OpenDP Library, a widely used open source library for differential privacy. His work involves collaborating with researchers to adapt differentially private methods into trustworthy and accessible software tools, and communicating how these tools can be used.

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.