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 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
Publisher resources
Table of contents
- Preface
- 1. Welcome to Differential Privacy
- 2. Differential Privacy Fundamentals
- 3. Stable Transformations
- 4. Private Mechanisms
- 5. Definitions of Privacy
- 6. Fearless Combinators
- 7. Eyes on the Privacy Unit
- 8. Differentially Private Statistical Modeling
- 9. Differentially Private Machine Learning
- 10. Differentially Private Synthetic Data
- 11. Protecting Your Data Against Privacy Attacks
- 12. Defining Privacy Loss Parameters of a Data Release
- 13. Planning Your First DP Project
- Further Reading
- A. Supplementary Definitions
- B. Renyi Differential Privacy
- C. The Exponential Mechanism Satisfies Bounded Range
- D. Structured Query Language (SQL)
- E. Composition Proofs
- F. Machine Learning
- G. Where to Find Solutions
- About the Authors
Product information
- Title: Hands-On Differential Privacy
- Author(s):
- Release date: May 2024
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492097747
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