PRIVACY PRESERVING DATA SCIENCE WITH R: DIFFERENTIAL PRIVACY, DATA ANONYMIZATION, SYNTHETIC DATA, AND SECURE MACHINE LEARNING FOR REAL-WORLD DATA PROTECTION (THE APPLIED DATA SCIENCE WITH R SERIES)
Language: English
Published by Independently published, 2026
Series: Book 6 of 27 - THE APPLIED DATA SCIENCE WITH R SERIES
- Softcover
- New
Seller: California Books, Miami, FL, U.S.A.California Books
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- Title
- PRIVACY PRESERVING DATA SCIENCE WITH R: DIFFERENTIAL PRIVACY, DATA ANONYMIZATION, SYNTHETIC DATA, AND SECURE MACHINE LEARNING FOR REAL-WORLD DATA PROTECTION (THE APPLIED DATA SCIENCE WITH R SERIES)
- Author
- J. A., LAMINA
- Publisher
- Independently published
- Publication year
- 2026
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 13
- 9798253552711
- Series
- Book 6 of 27: THE APPLIED DATA SCIENCE WITH R SERIES
Privacy-Preserving Data Science with R: Differential Privacy, Data Anonymization, Synthetic Data, and Secure Machine Learning for Real-World Data Protection
No GPS? No problem. With this book, you'll always know what's ahead.
Most data science systems are built to extract insight not to protect people. That’s why they fail under real scrutiny. Data gets re-identified. Models leak information. Dashboards expose patterns that should never be visible. And by the time it’s discovered, the damage is already done.
This book takes a different approach.
It shows you how to design data systems that work in the real world where privacy is not optional, attackers are not hypothetical, and compliance is enforced.
Instead of theory, you get practical, production-ready workflows in R for building systems that protect sensitive data without destroying analytical value.
Inside this book, you will learn how to:
- Identify and eliminate re-identification risks before modeling begins
- Build anonymization pipelines that hold up against real attacks
- Apply differential privacy with controlled privacy budgets
- Generate synthetic data without leaking original records
- Secure machine learning models against inference attacks
- Replace raw data access with safe query systems and APIs
- Design privacy-first data pipelines from ingestion to deployment
- Measure privacy and utility using defensible, audit-ready metrics
- Build GDPR-ready and HIPAA-aware data systems
- Simulate real-world attacks and harden your systems against them
This is not a theoretical guide. It is a hands-on blueprint for engineers, data scientists, and analysts who need to use sensitive data without exposing it.
Unlike most books in this space, this guide focuses on:
- Real-world implementation in R not abstract frameworks
- End-to-end system design, not isolated techniques
- Attack simulation and defense, not just prevention
- Measurable privacy, not vague compliance claims
If you're working with customer data, healthcare data, financial data, or any system where privacy matters, this book gives you the tools to build systems that are secure, scalable, and defensible.
Because in modern data science, it’s not enough to build models that work.
You have to build systems that survive scrutiny.
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