Practical Synthetic Data Generation
Balancing Privacy and the Broad Availability of Data
Khaled El Emam ; Lucy Mosquera ; Richard Hoptroff
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. Les mer
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(Paperback)
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På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering
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