Data Privacy: Implementing privacy frameworks and machine learning models across AI, blockchain, healthcare, and IoT ecosystems (English Edition)

Data is now the fuel of every industry, from healthcare and automotive to smart homes and AI-powered services. As connected devices, cloud platforms, and machine learning spread everywhere, privacy and security risks silently grow alongside innovation.

Guided by real-world scenarios, the book moves from the origins of data privacy and regulatory frameworks to practical data classification, anonymization, and masking techniques you can implement. You will learn how automation, AI, and ML interact with privacy; how blockchain can both enhance and endanger data protection; how to secure IoT ecosystems and healthcare data; and how to manage privacy in automotive and smart mobility, including attack tools such as Flipper Zero. Finally, you will build a unifying privacy framework that ties together standards, governance, and hands-on controls across all these domains.

By the end of this book, readers will be able to analyze and classify data, design and evaluate privacy controls. They will be equipped to translate privacy principles into concrete architectures, policies, and safeguards that make a measurable difference in their daily work, whatever their sector.

What you will learn

● Classify and map data to effective, risk-based protection measures.

● Apply anonymization, masking, swapping, and synthetic data for privacy preservation.

● Evaluate blockchain, IoT, and AI architectures for privacy risks.

● Design controls for healthcare, automotive, and smart home ecosystems.

● Translate regulations into practical policies, procedures, and technical safeguards.

● Mitigate DoS attacks on IoT physical layers and wireless sensors.

Who this book is for

This book is for privacy professionals, cybersecurity specialists, data protection officers, compliance managers, solution architects, and technical leads working with AI, IoT, cloud, or blockchain systems. It is also valuable for auditors, consultants, product managers, and engineers responsible for designing or assessing data-intensive services.

Table of Contents

1. Origin of Data Privacy

2. The Steady State

3. Data Classification

4. Impact of Privacy Laws on Data Activities

5. Anonymization

6. Rise of Automation

7. Machine Learning and Secure Programming

8. Privacy in Blockchain

9. Embedding Privacy in Blockchain

10. Privacy in Healthcare

11. Privacy and Security in Internet of Things

12. Privacy in Automotive

13. Setting up a Proper Privacy Framework with Monster Mesh

14. Upcoming Future

15. Case Studies