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

by Khaled El Emam , Lucy Mosquera , and Richard Hoptroff

Copyright 2020 K Sharp Technology Inc., Lucy Mosquera, and Richard Hoptroff. All rights reserved.

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978-1-492-07274-4

[LSI]

Interest in synthetic data has been growing rapidly over the last few years. This interest has been driven by two simultaneous trends. The first is the demand for large amounts of data to train and build artificial intelligence and machine learning (AIML) models. The second is recent work that has demonstrated effective methods for generating high-quality synthetic data. Both have resulted in the recognition that synthetic data can solve some difficult problems quite effectively, especially within the AIML community. Companies like NVIDIA, IBM, and Alphabet, as well as agencies such as the US Census Bureau, have adopted different types of data synthesis methodologies to support model building, application development, and data dissemination .

This book provides you with a gentle introduction to methods for the following: generating synthetic data, evaluating the data that has been synthesized, understanding the privacy implications of synthetic data, and implementing synthetic data within your organization. We show how synthetic data can accelerate AIML projects. Some of the problems that can be tackled by having synthetic data would be too costly or dangerous to solve using more traditional methods (e.g., training models controlling autonomous vehicles), or simply cannot be done otherwise. We also explain how to assess the privacy risks from synthetic data, even though they tend to be minimal if synthesis is done properly.

While we want this book to be an introduction, we also want it to be applied. Therefore, we will discuss some of the issues that will be encountered with real data, not curated or cleaned data. Real data is complex and messy, and data synthesis needs to be able to work within that context.

Our intended audience is analytics leaders who are responsible for enabling AIML model development and application within their organizations, as well as data scientists who want to learn how data synthesis can be a useful tool for their work. We will use examples of different types of data synthesis to illustrate the broad applicability of this approach. Our main focus here is on the synthesis of structured data.