Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data (1)

By: Khaled El Emam and Lucy Mosquera and Richard Hoptroff

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Synopsis: 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—fake 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 distributionsMethods for distribution fitting covering different goodness-of-fit metricsHow to replicate the simple structure of original dataAn approach for modeling data structure to consider complex relationshipsMultiple approaches and metrics you can use to assess data utilityHow analysis performed on real data can be replicated with synthetic dataPrivacy implications of synthetic data and methods to assess identity disclosure