This hands-on tutorial will take participants beyond the basics of the Synthetic Data SDK, the emerging open-source standard for creating privacy-preserving synthetic data.

After a brief recap of the SDK’s core capabilities, the session will dive into advanced functionality, beginning with an in-depth exploration of differential privacy. Attendees will learn how the SDK integrates formal privacy guarantees, configure key parameters (i.e., epsilon and delta), and observe the trade-offs between privacy and utility through live examples.

The session will then focus on conditional generation, demonstrating how users can guide synthetic data output based on specific constraints or target values - an essential feature for scenario testing and AI model validation.

A dedicated section will cover multi-table synthesis, where participants will learn how to model and generate relational datasets with primary-foreign key dependencies, preserving structural and statistical integrity across multiple linked tables.

Finally, the tutorial will introduce the concept of fair synthetic data, showing how the SDK supports data generation aligned with the principle of statistical parity to help reduce representational bias in downstream use cases.

Each segment includes interactive coding exercises and real-world datasets to ensure practical understanding. Participants should have a working knowledge of Python and prior experience with the SDK or similar tools.