Data Privacy

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.

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

Walter Rocchi is a seasoned compliance, governance, privacy, risk management and cybersecurity professional with over 26 years of experience. He specializes in building practical governance frameworks that connect ISO standards, EU regulations, and real-world technology projects. As a third-party auditor and consultant, he has implemented and assessed management systems across ISO 27001, ISO 42001, ISO 56001 and related frameworks in sectors ranging from medical devices and life insurance to AI-driven digital services, working with organizations such as the European Central Bank, Vodafone, Adidas, Deutsche Bank, Deutsche Telekom, Deloitte and many others.

Acting both as architect and developer, Walter designs automation solutions for document processing, OCR and compliance monitoring, turning complex requirements such as the EU AI Act, NIS2 and DORA into repeatable workflows and tools. His work includes holistic control mappings that align ISO standards, the NIST AI Risk Management Framework and data protection requirements, enabling organizations to move rapidly from gap analysis to operational governance without losing rigor.

Passionate about knowledge sharing, he writes hands-on guides and training materials that make advanced topics—AI governance, innovation management and risk-based auditing— accessible to practitioners. His books and courses are aimed at privacy professionals, auditors, engineers and managers who need concrete templates, examples and step-by-step methods rather than abstract theory, helping them apply privacy and security principles in everyday projects.