Supporting Database Constraints in Synthetic Data Generation based on Generative Adversarial Networks
Summary: Extends Tabular GAN (TGAN) to enforce relational DB constraints during synthetic data generation, enabling constraint-preserving data for privacy benchmarking. Prototype evaluates extensions on real schemas, showing fidelity-constraint tradeoffs. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Wanxin Li
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,712 | Accelerating Approximate Aggregation Queries with Expensive Predicates | 2021 | VLDB | 5.9787986e-05 |
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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 |
|---|---|---|---|---|
| 560 | Dependencies Revisited for Improving Data Quality | 2008 | PODS | 0.00020141923 |
| 2,421 | Data Synthesis based on Generative Adversarial Networks | 2018 | VLDB | 8.8514021e-05 |
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| 8,280 | Synthetic Tabular Data: Methods, Attacks and Defenses | 2025 | VLDB | 4.5435639e-05 |
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| 2,421 | Data Synthesis based on Generative Adversarial Networks | 2018 | VLDB | 8.8514021e-05 |
| 4,884 | Relational Data Synthesis using Generative Adversarial Networks: A Design Space Exploration | 2020 | VLDB | 5.8540287e-05 |