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This work presents a systematic benchmark of differentially private synthetic data generation algorithms that can generate tabular data.
C. Arnold and M. Neunhoeffer · 2004
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Privsyn: Differentially private data synthesis
Z. Zhang, T. Wang, N. Li, J. Honorio, M. Backes, S. He, J. Chen, and Y. Zhang · 2012
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A bias-correction for cramér’s v and tschuprow’s t
W. Bergsma · 2013
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Dpsynthesizer: Differentially private data synthesizer for privacy preserving data sharing
H. Li, L. Xiong, L. Zhang, and X. Jiang · 2014
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Privbayes: private data release via bayesian networks
J. Zhang, G. Cormode, C. M. Procopiuc, D. Srivastava, and X. Xiao · 2014
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Principled evaluation of differentially private algorithms using dpbench
M. Hay, A. Machanavajjhala, G. Miklau, Y. Chen, and D. Zhang · 2016
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Privtree: A differentially private algorithm for hierarchical decompositions
J. Zhang, X. Xiao, and X. Xie · 2016
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UCI machine learning repository, 2017
D. Dua and C. Graff · 2017
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Privbayes: Private data release via bayesian networks
J. Zhang, G. Cormode, C. M. Procopiuc, D. Srivastava, and X. Xiao · 2017
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Implicit maximum likelihood estimation
K. Li and J. Malik · 2018
Earlier work this paper cites.
pmse mechanism: Differentially private synthetic data with maximal distributional similarity
J. Snoke and A. B. Slavkovic · 2018
Earlier work this paper cites.
Differentially private generative adversarial network
L. Xie, K. Lin, S. Wang, F. Wang, and J. Zhou · 2018
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C. M. Bowen and J. Snoke · 2019
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Ron-gauss: Enhancing utility in non-interactive private data release
T. Chanyaswad, C. Liu, and P. Mittal · 2019
Earlier work this paper cites.
Impelementation of Kamino
C. Ge · 2019
Cited alongside, same era.
PATE-GAN: generating synthetic data with differential privacy guarantees
J. Jordon, J. Yoon, and M. van der Schaar · 2019
Cited alongside, same era.
Graphical-model based estimation and inference for differential privacy
R. McKenna, D. Sheldon, and G. Miklau · 2019
Cited alongside, same era.
Impelementation of PrivBayes
SDGym · 2019
Cited alongside, same era.
DP-CGAN: differentially private synthetic data and label generation
R. Torkzadehmahani, P. Kairouz, and B. Paten · 2019
Cited alongside, same era.
Modeling tabular data using conditional gan
L. Xu, M. Skoularidou, A. Cuesta-Infante, and K. Veeramachaneni · 2019
Cited alongside, same era.
https://github.com/sdv-dev/SDGym , 2021
SDGym · 2021
Closest in time.
Differentially private query release through adaptive projection
S. Aydöre, W. Brown, M. Kearns, K. Kenthapadi, L. Melis, A. Roth, and A. A. Siva · 2021
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Impelementation of RonGauss
BorealisAI · 2021
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Kamino: Constraint-aware differentially private data synthesis
C. Ge, S. Mohapatra, X. He, and I. F. Ilyas · 2021
Closest in time.
DP-MERF: differentially private mean embeddings with randomfeatures for practical privacy-preserving data generation
F. Harder, K. Adamczewski, and M. Park · 2021
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Dpsyn: Experiences in the NIST differential privacy data synthesis challenges
N. Li, Z. Zhang, and T. Wang · 2021
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Comparative study of differentially private data synthesis methods
C. M. Bowen and F. Liu · 2020
Cited alongside, same era.
Gs-wgan: A gradient-sanitized approach for learning differentially private generators
D. Chen, T. Orekondy, and M. Fritz · 2020
Cited alongside, same era.
Relational data synthesis using generative adversarial networks: A design space exploration
J. Fan, T. Liu, G. Li, J. Chen, Y. Shen, and X. Du · 2020
Cited alongside, same era.
Differentially private synthetic data: Applied evaluations and enhancements
L. Rosenblatt, X. Liu, S. Pouyanfar, E. de Leon, A. Desai, and J. Allen · 2020
Cited alongside, same era.
New oracle-efficient algorithms for private synthetic data release
G. Vietri, G. Tian, M. Bun, T. Steinke, and Z. S. Wu · 2020
Cited alongside, same era.
https://github.com/gretelai/gretel-synthetics/blob/master/examples/data/uber˙scooter˙rides˙1day.csv , 2020
Scooter Dataset · 2021
Cited alongside, same era.
Closest in time.
Leveraging public data for practical private query release
T. Liu, G. Vietri, T. Steinke, J. R. Ullman, and Z. S. Wu · 2021
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Impelementation of MST
R. McKenna · 2021
Closest in time.
Impelementation of MWEM-PGM
R. McKenna · 2021
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Winning the NIST contest: A scalable and general approach to differentially private synthetic data
R. McKenna, G. Miklau, and D. Sheldon · 2021
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Impelementation of RAP
A. Research · 2021
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Impelementation of DPGAN, DPCTGAN, PATEGAN and PATECTGAN
SmartNoise · 2021
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Impelementation of FEM
G. Vietri · 2021
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