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

Description: FREE SHIPPING UK WIDE Hands-On Differential Privacy by Ethan Cowan, Mayana Pereira, Michael Shoemate Many organizations today analyze and share large, sensitive datasets about individuals. Whether these datasets cover healthcare details, financial records, or exam scores, its become more difficult for organizations to protect an individuals information. This practical book explains how differential privacy (DP) can help. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description Many organizations today analyze and share large, sensitive datasets about individuals. Whether these datasets cover healthcare details, financial records, or exam scores, its become more difficult for organizations to protect an individuals 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. Youll 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, youll learn: How DP guarantees privacy when other data anonymization methods dont 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 Author Biography 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. Hes also built a collection of reusable modular UI components hes 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 Microsofts 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. Details ISBN1492097748 Publisher OReilly Media Language English ISBN-10 1492097748 ISBN-13 9781492097747 Format Paperback Year 2024 Imprint OReilly Media Audience General Author Michael Shoemate Pages 275 Subtitle Introduction to the Theory and Practice Using Opendp Place of Publication Sebastopol Country of Publication United States Publication Date 2024-05-31 US Release Date 2024-05-31 UK Release Date 2024-05-31 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! 30 DAY RETURN POLICY No questions asked, 30 day returns! FREE DELIVERY No matter where you are in the UK, delivery is free. SECURE PAYMENT Peace of mind by paying through PayPal and eBay Buyer Protection TheNile_Item_ID:160373495;

Price: 61.8 GBP

Location: London

End Time: 2025-02-05T00:47:48.000Z

Shipping Cost: 7.37 GBP