Generating synthetic data often carries the goal of preserving the privacy of the original dataset's subjects. However, simply generating data isn't enough; we must rigorously assess how much privacy is actually maintained. Failure to do so can lead to risks such as reconstructing sensitive information or identifying individuals present in the original data.

This chapter focuses on practical techniques for quantifying these privacy risks. You will learn about:

• Potential privacy vulnerabilities: Understanding how synthetic data can inadvertently leak information.
• Membership Inference Attacks (MIAs): Methods to determine if a specific individual's data was used to train the generative model.
• Attribute Inference Attacks: Techniques assessing the possibility of inferring sensitive attributes about individuals, even if their membership isn't confirmed.
• Distance-Based Privacy Metrics: Using proximity measures between synthetic and real records as indicators of privacy risk.
• Differential Privacy Considerations: Evaluating privacy if specific mechanisms like differential privacy were employed during data generation.

By the end of this chapter, you will be equipped to implement and interpret several key methods for measuring the privacy characteristics of your synthetic datasets.