PrivBayes: Private Data Release via Bayesian Networks
Summary: PrivBayes builds a Bayesian network to model attribute correlations and releases synthetic data by perturbing marginals and sampling the resulting distribution. Surrogate mutual-information learning guides BN construction boosting marginal accuracy. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jun Zhang (Nanyang Technological University)
- 2. Graham Cormode (University of Warwick)
- 3. Cecilia M. Procopiuc (AT&T)
- 4. Divesh Srivastava (AT&T)
- 5. Xiaokui Xiao (Nanyang Technological University)
BibTeX Citation
@inproceedings{zhang_sigmod14,
title = {{PrivBayes: Private Data Release via Bayesian Networks}},
author = {Zhang, Jun and Cormode, Graham and Procopiuc, Cecilia M. and Srivastava, Divesh and Xiao, Xiaokui},
series = {{SIGMOD} '14},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2588555.2588573},
url = {https://dl.acm.org/doi/10.1145/2588555.2588573},
year = {2014}
}
Incoming Citations (Sorted by Pagerank)
Showing 19 of 19 citing papers.
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 2,856 | Publishing Set-Valued Data via Differential Privacy | 2011 | VLDB |
| 2 | 4,037 | Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge | 2007 | VLDB |
| 3 | 1,837 | Marginal Release Under Local Differential Privacy | 2018 | SIGMOD |
| 4 | 7,841 | Quantifying identifiability to choose and audit epsilon in differentially private deep learning | 2021 | VLDB |
| 5 | 6,701 | Information Preservation in Statistical Privacy and Bayesian Estimation of Unattributed Histograms | 2013 | SIGMOD |
| 6 | 3,016 | Plausible Deniability for Privacy-Preserving Data Synthesis | 2017 | VLDB |
| 7 | 2,476 | Data Synthesis via Differentially Private Markov Random Fields | 2021 | VLDB |
| 8 | 12,545 | Publishing Naive Bayesian Classifiers: Privacy without Accuracy Loss | 2009 | VLDB |
| 9 | 3,375 | Bayesian Differential Privacy on Correlated Data | 2015 | SIGMOD |
| 10 | 10,964 | Balancing Privacy and Utility in Correlated Data: A Study of Bayesian Differential Privacy | 2025 | VLDB |