PSynDB: Accurate and Accessible Private Data Generation
Summary: PSynDB: a web-based synthetic-table generator with differential privacy, delivering high-accuracy data for user-defined analytics. Users can browse expected error rates to tune the privacy budget, and PSynDB outputs a portable data-synthesis program for trusted-environment data generation. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zhiqi Huang
- 2. Ryan McKenna
- 3. George Bissias
- 4. Gerome Miklau
- 5. Michael Hay
- 6. Ashwin Machanavajjhala
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,329 | AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data | 2022 | VLDB | 7.2156424e-05 |
| 10,798 | PrivEval: a tool for interactive evaluation of privacy metrics in synthetic data generation | 2025 | VLDB | 4.1945683e-05 |
| 11,143 | DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms | 2024 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 136 | Revealing Information while Preserving Privacy | 2003 | PODS | 0.0004241101 |
| 742 | Optimizing Linear Counting Queries Under Differential Privacy | 2010 | PODS | 0.00017360873 |
| 2,434 | Optimizing error of high-dimensional statistical queries under differential privacy | 2018 | VLDB | 8.8278955e-05 |
| 4,502 | ϵktelo: A Framework for Defining Differentially-Private Computations | 2018 | SIGMOD | 6.1366984e-05 |
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