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

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
ASIN: 1492072745
VSKU: GBV.1492072745.G
Condition: Good
Author/Artist:Mosquera, Lucy|Emam, Khaled El|Hoptroff, Richard
Binding: Paperback
Note: Any images shown are stock photographs and product may differ from what is shown.
Condition Notes: Book has corner edge dings and or scratches and signs of light wear.