Hands-On Differential Privacy by Ethan Cowan
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.
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.
| SKU | Unavailable |
| ISBN 13 | 9781492097747 |
| ISBN 10 | 1492097748 |
| Title | Hands-On Differential Privacy |
| Author | Ethan Cowan |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | O'Reilly Media |
| Year published | 2024-05-31 |
| Number of pages | 275 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |