Practical synthetic data generation : balancing privacy and the broad availability of data

By:

Emam, Khaled El

By:

Publication details: USA: Shroff/O'Reilly, 2020Description: 151ISBN:

9788194435013

Subject(s):

DDC classification:

006.312 EMA

Summary: Home/Books/Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data Publisher: Shroff/O'Reilly ISBN: 9788194435013 You Pay: ₹67500 Leadtime to ship in days (default): ships in 1-2 days Ships only in the (Bangladesh, India, Nepal, Pakistan, Sri Lanka) In Stock Quantity: 1 Minimum quantity for "Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data" is 1. Add to wish list Compare Share — Different payment methods — Best price Description Features Table of Contents Reviews All Indian Reprints of O'Reilly are printed in Grayscale 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

List(s) this item appears in: New Arrivals for the Month of September - 2023

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Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	Alliance College of Engineering and Design	CSE & IT	006.312 EMA (Browse shelf(Opens below))	Available		E12493

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Home/Books/Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data
Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data
Publisher: Shroff/O'Reilly
ISBN:
9788194435013
You Pay: ₹67500
Leadtime to ship in days (default): ships in 1-2 days
Ships only in the (Bangladesh, India, Nepal, Pakistan, Sri Lanka)
In Stock
Quantity:
1
Minimum quantity for "Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data" is 1.

Add to wish list
Compare
Share

— Different payment methods
— Best price

Description
Features
Table of Contents
Reviews
All Indian Reprints of O'Reilly are printed in Grayscale

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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