This book is all about providing you the key concepts behind privacy engineering that you might face in your day to day operations as a non-technical privacy professional.
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Greetings, and welcome to the second installment of the HGPE Trilogy, "Privacy Engineering Methodologies: A Non-Tech Introduction."
This book is designed to help legal, technical, and product teams become familiar with implementing privacy by design principles using privacy engineering methodologies.
This book is your field guide to the world of Privacy Engineering, tailored for those who may not have a technical background.
Here, we'll delve into the essentials of Privacy Engineering Methodologies, making this complex subject accessible and engaging for all readers.
✅ Digital Identities: Understand the concept of digital identities and their significance in today's interconnected world. Explore the mechanisms behind the creation and management of digital identities.
✅ Data Sharing: Gain insights into the intricacies of data sharing and its role in the digital landscape. Learn how to navigate the challenges and best practices associated with responsible data sharing.
✅ Privacy-Enhancing Technologies: Discover the world of privacy-enhancing technologies (PETs) and their applications in safeguarding data. Explore various PETs and how they can be leveraged to protect privacy in different contexts.
✅ Data Mapping: Delve into the process of data mapping and its relevance in privacy engineering. Learn how to create effective data maps to enhance data management and privacy practices.
✅ Encryption: Explore the fundamental principles of encryption and its role in securing sensitive information. Gain practical knowledge of encryption techniques and their real-world applications.
✅ Data Masking: Understand the concept of data masking and its role in anonymizing sensitive data. Explore strategies for implementing data masking techniques to protect privacy.
✅ Privacy Threat Modeling: Learn about the art of privacy threat modeling and its importance in identifying potential risks. Apply LINDUNN and Solove's Taxonomy to effectively analyze and mitigate privacy threats.
✅ De-Identification: Gain a deep understanding of de-identification techniques and their role in preserving privacy. Learn how to de-identify data while maintaining data utility and compliance with privacy regulations.
