Privacy Engineering Guide
Overview
Welcome to the AppSec Atlas guide on Privacy Engineering. As data protection regulations like GDPR, CCPA, and HIPAA become stricter, the need to build privacy-preserving systems is paramount. Privacy engineering bridges the gap between legal requirements and technical implementation, integrating privacy controls directly into system architectures and data flows.
Prerequisites
- Basic understanding of cryptography and data protection.
- Familiarity with data structures and database systems.
- Knowledge of Python for running differential privacy and homomorphic encryption code examples.
Learning Objectives
- Understand Privacy by Design (PbD) principles and how to embed them into applications.
- Master anonymization techniques including k-anonymity, l-diversity, and t-closeness.
- Implement Differential Privacy mechanisms to protect statistical data releases.
- Explore advanced Privacy Enhancing Technologies (PETs) like Fully Homomorphic Encryption (FHE) and Secure Multi-Party Computation (SMPC).
- Learn how to automate Data Subject Access Requests (DSAR) and manage consent.
- Conduct Privacy Impact Assessments (PIAs) and apply the LINDDUN threat modeling methodology.