Really Useful Synthetic Data -- A Framework to Evaluate the Quality of
Differentially Private Synthetic Data

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Really Useful Synthetic Data -- A Framework to Evaluate the Quality of Differentially Private Synthetic Data

16 April 2020

Christian Arnold

Marcel Neunhoeffer

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Papers citing "Really Useful Synthetic Data -- A Framework to Evaluate the Quality of Differentially Private Synthetic Data"

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Title
Generating tabular datasets under differential privacy G. Truda DiffM 66 6 0 28 Aug 2023
30 Years of Synthetic Data Joerg Drechsler Anna Haensch 72 16 0 04 Apr 2023
Statistical Data Privacy: A Song of Privacy and Utility Aleksandra B. Slavkovic Jeremy Seeman 58 27 0 06 May 2022
Benchmarking Differentially Private Synthetic Data Generation Algorithms Yuchao Tao Ryan McKenna Michael Hay Ashwin Machanavajjhala G. Miklau SyDa 105 87 0 16 Dec 2021
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data Ryan McKenna G. Miklau Daniel Sheldon SyDa 92 127 0 11 Aug 2021
Synthetic Data -- Anonymisation Groundhog Day Theresa Stadler Bristena Oprisanu Carmela Troncoso 93 161 0 13 Nov 2020
Differentially Private Synthetic Data: Applied Evaluations and Enhancements Lucas Rosenblatt Xiao-Yang Liu Samira Pouyanfar Eduardo de Leon Anuj M. Desai Joshua Allen SyDa 74 67 0 11 Nov 2020
Private Post-GAN Boosting Marcel Neunhoeffer Zhiwei Steven Wu Cynthia Dwork 199 29 0 23 Jul 2020