Synthetic Tabular Data: Methods, Attacks and Defenses
Summary: Comprehensive tutorial-survey of tabular synthetic data methods spanning probabilistic graphical models, deep generative models, and generative-AI conditioning, comparing statistical, ML and generative objectives and modeling tradeoffs. Analyzes privacy risks via attacks, links empirical attack success to formal defenses (e.g., differential privacy), and highlights open problems for utility–privacy evaluation. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Graham Cormode
- 2. Shripad Gade
- 3. Samuel Maddock
- 4. Enayat Ullah
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,262 | Understanding Disclosure Risk in Differential Privacy with Applications to Noise Calibration and Auditing | 2026 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,881 | Data Synthesis via Differentially Private Markov Random Fields | 2021 | VLDB | 7.9665978e-05 |
| 3,329 | AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data | 2022 | VLDB | 7.2156424e-05 |
| 5,349 | PrivLava: Synthesizing Relational Data with Foreign Keys under Differential Privacy | 2023 | SIGMOD | 5.553869e-05 |
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