Programming Differential Privacy
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Joseph P Near, University of Vermont
Chiké Abuah, University of Vermont
Copyright Year:
Publisher: Joseph P. Near and Chiké Abuah
Language: English
Formats Available
Conditions of Use
Attribution-NonCommercial-ShareAlike
CC BY-NC-SA
Table of Contents
• Cover
• Front Matter
• Introduction
• De-Identification
• K-Anonymity
• Differential Privacy
• Properties Of Differential Privacy
• Sensitivity
• Approximate Differential Privacy
• Local Sensitivity
• Variants Of Differential Privacy
• The Exponential Mechanism
• The Sparse Vector Technique
• Design & Deployment
• Machine Learning
• Local Differential Privacy
• Synthetic Data
• Efficiency & Limitations
• Further Reading
• License
About the Book
This book offers a hands-on introduction to differential privacy, combining formal foundations with executable code and practical design patterns.
The material has evolved through lecture notes, workshops, and collaborations with academics and practitioners, gradually expanding to cover both classical and modern topics.
Accessibility
The PDF contains selectable text (not just scanned images), which means screen readers and text-to-speech tools can read the content. This is a fundamental accessibility baseline for PDFs. Chapters, sections, and a table of contents are present, which can help users orient themselves within the document.
About the Contributors
Authors
Joseph is an Associate Professor of Computer Science at the University of Vermont. His research interests include data privacy, computer security, and programming languages. Joseph received his BS in computer science from Indiana University and his MS and PhD in computer science from MIT.
Chiké Abuah is a computer scientist, specializing in formal verification, generative AI, and program analysis. He received his PhD in computer science at the University of Vermont. Currently he researches and implements tools which help programmers build reliable software.