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


Practical Synthetic Data Generation
By 作者: Khaled El Emam
Pages 页数: 175 pages
Edition 版本: 1
Language 语言: English
Publisher Finelybook 出版社: O'Reilly Media
Publication Date 出版日期: 2020-06-30
ISBN-10 书号:1492072745
ISBN-13 书号:9781492072744
The Book Description robot was collected from Amazon and arranged by Finelybook

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 distributions
Methods for distribution fitting covering different goodness-of-fit metrics
How to replicate 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 replicated with synthetic data
Privacy implications of synthetic data and methods to assess identity disclosure

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