PrivPetal: Relational Data Synthesis via Permutation Relations
Summary: PrivPetal uses permutation relations to synthesize a flattened view, then decomposes to base tables, avoiding DP on join keys. A refined Markov random field with fine-grained privacy analysis delivers high-utility synthetic data, beating PrivLava on aggregate queries across real data and TPC-H. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Kuntai Cai (National University of Singapore)
- 2. Xiaokui Xiao (National University of Singapore)
- 3. Yin Yang (Hamad Bin Khalifa University)
BibTeX Citation
@inproceedings{cai_sigmod25,
title = {{PrivPetal: Relational Data Synthesis via Permutation Relations}},
author = {Cai, Kuntai and Xiao, Xiaokui and Yang, Yin},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725341},
url = {https://dl.acm.org/doi/10.1145/3725341},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 281 | Towards Practical Differential Privacy for SQL Queries | 2018 | VLDB | 0.00022445849 |
| 2,129 | Data Synthesis based on Generative Adversarial Networks | 2018 | VLDB | 9.1266572e-05 |
| 2,476 | Data Synthesis via Differentially Private Markov Random Fields | 2021 | VLDB | 8.5258582e-05 |
| 2,841 | AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data | 2022 | VLDB | 8.0637668e-05 |
| 3,016 | Plausible Deniability for Privacy-Preserving Data Synthesis | 2017 | VLDB | 7.8484858e-05 |
| 3,412 | Kamino: Constraint-Aware Differentially Private Data Synthesis | 2021 | VLDB | 7.4344343e-05 |
| 4,827 | PrivLava: Synthesizing Relational Data with Foreign Keys under Differential Privacy | 2023 | SIGMOD | 6.4895329e-05 |
| 6,755 | Synthesizing Linked Data Under Cardinality and Integrity Constraints | 2021 | SIGMOD | 5.7830386e-05 |
| 7,850 | Differentially Private Data Release over Multiple Tables | 2023 | PODS | 5.5316993e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,647 | Private Synthetic Data Generation in Bounded Memory | 2025 | PODS |
| 2 | 6,534 | Better than Composition: How to Answer Multiple Relational Queries under Differential Privacy | 2023 | SIGMOD |
| 3 | 7,850 | Differentially Private Data Release over Multiple Tables | 2023 | PODS |
| 4 | 855 | Relationship Privacy: Output Perturbation for Queries with Joins | 2009 | PODS |
| 5 | 3,016 | Plausible Deniability for Privacy-Preserving Data Synthesis | 2017 | VLDB |
| 6 | 10,282 | Prism: Private Relational Data Synthesis with Language Models | 2026 | SIGMOD |
| 7 | 1,144 | PrivateSQL: A Differentially Private SQL Query Engine | 2019 | VLDB |
| 8 | 10,967 | Privacy-Enhanced Database Synthesis for Benchmark Publishing | 2025 | VLDB |
| 9 | 2,476 | Data Synthesis via Differentially Private Markov Random Fields | 2021 | VLDB |
| 10 | 4,827 | PrivLava: Synthesizing Relational Data with Foreign Keys under Differential Privacy | 2023 | SIGMOD |