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)
Publisher: Independently published
Publication Date: 2026
Language: English
ISBN 13: 9798253552711
Binding: Soft cover
Condition: New

About this title

Synopsis

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.

"About this title" may belong to another edition of this title.

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