Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data, Khaled El Emam,

Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data― data generated from real data―so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue.

Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution.

This book describes:

Steps for generating synthetic data using multivariate normal distributions

Methods for distribution fitting covering different goodness-of-fit metrics

How to te the simple structure of original data

An approach for modeling data structure to consider complex relationships

Multiple approaches and metrics you can use to assess data utility

How analysis performed on real data can be ted with synthetic data

Privacy implications of synthetic data and methods to assess identity disclosure

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