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Privacy-Enhanced Database Synthesis for Benchmark Publishing
Summary: PrivBench: DP synthesis with sum-product networks producing benchmark-ready DBs that preserve data distribution and query runtime characteristics. Supports multi-relation referential schemas, proves database-level DP, and empirically improves distributional and runtime fidelity versus prior methods.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 14031
- Venue
- VLDB
- Year
- 2025
- Pagerank
- 4.1945683e-05
- Overall Rank
- 10,724 | 25.40%
- DOI
-
10.14778/3705829.3705855
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Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059038975 |
| 204 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034784455 |
| 608 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019235898 |
| 629 |
Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors |
2009 |
VLDB |
0.00018942366 |
| 910 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423056 |
| 1,638 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00011049779 |
| 1,738 |
PrivateSQL: A Differentially Private SQL Query Engine |
2019 |
VLDB |
0.00010720057 |
| 1,891 |
Towards Model-based Pricing for Machine Learning in a Data Marketplace |
2019 |
SIGMOD |
0.00010194092 |
| 2,881 |
Data Synthesis via Differentially Private Markov Random Fields |
2021 |
VLDB |
7.9665978e-05 |
| 3,104 |
Computing Local Sensitivities of Counting Queries with Joins |
2020 |
SIGMOD |
7.5578613e-05 |
| 3,329 |
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data |
2022 |
VLDB |
7.2156424e-05 |
| 3,831 |
Kamino: Constraint-Aware Differentially Private Data Synthesis |
2021 |
VLDB |
6.7181688e-05 |
| 3,836 |
Dealer: An End-to-End Model Marketplace with Differential Privacy |
2021 |
VLDB |
6.7153977e-05 |
| 4,252 |
DPSynthesizer: Differentially Private Data Synthesizer for Privacy Preserving Data Sharing |
2014 |
VLDB |
6.3233894e-05 |
| 4,753 |
Secure Shapley Value for Cross-Silo Federated Learning |
2023 |
VLDB |
5.9469115e-05 |
| 5,349 |
PrivLava: Synthesizing Relational Data with Foreign Keys under Differential Privacy |
2023 |
SIGMOD |
5.553869e-05 |
| 5,491 |
R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys |
2022 |
SIGMOD |
5.4776364e-05 |
| 5,942 |
SAM: Database Generation from Query Workloads with Supervised Autoregressive Models |
2022 |
SIGMOD |
5.2634242e-05 |
| 8,609 |
PreFair: Privately Generating Justifiably Fair Synthetic Data |
2023 |
VLDB |
4.4853979e-05 |
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PrivateSQL: A Differentially Private SQL Query Engine |
2019 |
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| 7,864 |
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| 6,970 |
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PrivLava: Synthesizing Relational Data with Foreign Keys under Differential Privacy |
2023 |
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| 7,417 |
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SIGMOD |
4.7355114e-05 |
| 2,465 |
Principled Evaluation of Differentially Private Algorithms using DPBench |
2016 |
SIGMOD |
8.7518123e-05 |
| 7,502 |
PSynDB: Accurate and Accessible Private Data Generation |
2019 |
VLDB |
4.7180617e-05 |
| 10,500 |
PrivPetal: Relational Data Synthesis via Permutation Relations |
2025 |
SIGMOD |
4.1945683e-05 |
| 10,053 |
Benchmarking Differentially Private Tabular Data Synthesis: [Experiments & Analysis] |
2026 |
SIGMOD |
4.1945683e-05 |