Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data Contributor(s): El Emam, Khaled (Author), Mosquera, Lucy (Author), Hoptroff, Richard (Author) |
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ISBN: 1492072745 ISBN-13: 9781492072744 Publisher: O'Reilly Media OUR PRICE: $59.39 Product Type: Paperback - Other Formats Published: June 2020 |
Additional Information |
BISAC Categories: - Computers | Intelligence (ai) & Semantics - Computers | Databases - Data Mining - Computers | Security - Cryptography |
Dewey: 006.312 |
LCCN: 2021278037 |
Physical Information: 0.35" H x 7" W x 9.19" (0.60 lbs) 163 pages |
Descriptions, Reviews, Etc. |
Publisher Description: 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:
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