Part 1: Introduction to Data Privacy and Machine Learning

This part provides an introduction to the fundamental concepts of data privacy and the distinction between sensitive data and personal sensitive data, along with the importance of data privacy regulations. The concept of privacy by design is discussed, emphasizing the proactive integration of privacy measures into systems and processes. Additionally, notable privacy breaches in major enterprise companies are examined, highlighting the potential consequences and risks associated with such incidents. This introduction sets the foundation for understanding the significance of data privacy and the need for robust privacy measures. This part also covers privacy threat modeling using the LINDDUN framework in detail.

The second chapter in this part focuses on the different phases of the machine learning pipeline and the privacy threats and attacks that can occur at each stage. We will explore the phases of data collection, data preprocessing, model training, and inference. Within each phase, specific privacy threats and attacks, such as model inversion attacks and training data extraction attacks, are discussed in detail, providing illustrative examples. The importance of protecting training data privacy, input data privacy, model privacy, and inference/output data privacy is emphasized. This part highlights the potential risks and challenges associated with privacy in machine learning, underlining the need for robust privacy preservation techniques throughout the entire process. Exploration of privacy threats and attacks in each phase of the machine learning pipeline sheds light on the challenges of preserving privacy in machine learning systems.

This part has the following chapters:

• Chapter 1, Introduction to Data Privacy, Privacy Breaches, and Threat Modeling
• Chapter 2, Machine Learning Phases and Privacy Threats/Attacks in Each Phase