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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)

Paper ID
5846
Venue
SIGMOD
Year
2020
Pagerank
4.465684e-05
Overall Rank
8,699 | 39.49%
DOI
10.1145/3318464.3384414

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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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