Data Synthesis based on Generative Adversarial Networks
Summary: table-GAN synthesizes tabular data with GANs, preserving distribution while avoiding leakage. It shows model compatibility: synthetic data enables models to approach real-data performance, beating anonymization/perturbation across four real datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Noseong Park (University of North Carolina)
- 2. Mahmoud Mohammadi (University of North Carolina)
- 3. Kshitij Gorde (University of North Carolina)
- 4. Sushil Jajodia (George Mason University)
- 5. Hongkyu Park (Electronics and Telecommunications Research Institute)
- 6. Youngmin Kim (Electronics and Telecommunications Research Institute)
BibTeX Citation
@article{park_vldb18,
title = {{Data Synthesis based on Generative Adversarial Networks}},
author = {Park, Noseong and Mohammadi, Mahmoud and Gorde, Kshitij and Jajodia, Sushil and Park, Hongkyu and Kim, Youngmin},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {10},
pages = {1071--1083},
doi = {10.14778/3231751.3231757},
url = {https://doi.org/10.14778/3231751.3231757},
year = {2018}
}
Incoming Citations (Sorted by Pagerank)
Showing 16 of 16 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 70 | Privacy-Preserving Data Mining | 2000 | SIGMOD | 0.0003804755 |
| 244 | On the Design and Quantification of Privacy Preserving Data Mining Algorithms | 2001 | PODS | 0.00023476901 |
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