A Review Of Synthetic Data Generation Methods For Privacy Preserving Data Publishing

By H. Surendra, S. MohanH. 2017

72 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Kasia Chmielinski
2. Sarah Newman
3. Joshua Joseph
4. Ahmed Hosny
5. Samuel A. Assefa
6. Sarah Holland
7. Q. Liao
8. Ben Green

Citations in the corpus

• The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards (2018, background): “Synthetic data gives data specialists the ability to address incomplete data, and opens a potential path to privacy preserving data usage [34] .”
• Generating synthetic data in finance: opportunities, challenges and pitfalls (2020, methodology): “For a comprehensive survey of these methods see the survey by Surendra and Mohan [17].”
• Secure, privacy-preserving and federated machine learning in medical imaging (2020, unknown): “35 ), and the look-up table safe-kept separately.”