Benchmarking Differentially Private Tabular Data Synthesis: [Experiments & Analysis]
Summary: Benchmark and unified evaluation framework for DP tabular data synthesis that standardizes preprocessing, feature selection, and synthesis for fair, comprehensive comparisons. Module-level experiments reveal a utility–efficiency trade-off (statistical methods favor utility; deep models favor efficiency) and provide theoretical insights; code open-sourced. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Kai Chen
- 2. Xiaochen Li
- 3. Chen Gong
- 4. Ryan McKenna
- 5. Tianhao Wang
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,520 | PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions | 2016 | SIGMOD | 0.00011535148 |
| 2,434 | Optimizing error of high-dimensional statistical queries under differential privacy | 2018 | VLDB | 8.8278955e-05 |
| 2,881 | Data Synthesis via Differentially Private Markov Random Fields | 2021 | VLDB | 7.9665978e-05 |
| 3,304 | Plausible Deniability for Privacy-Preserving Data Synthesis | 2017 | VLDB | 7.2467347e-05 |
| 3,329 | AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data | 2022 | VLDB | 7.2156424e-05 |
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