HeteroFedSyn: Differentially Private Tabular Data Synthesis for Heterogeneous Federated Settings
Summary: HeteroFedSyn is the first DP tabular-data synthesis framework for heterogeneous horizontal federated learning, extending PrivSyn’s 2-way marginals. It combines noise-efficient projected dependency estimation, multiplicative-noise correction, and adaptive marginal selection to approach centralized utility. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Xiaochen Li (University of North Carolina at Greensboro)
- 2. Fengyu Gao (University of Virginia)
- 3. Xizixiang Wei (University of Virginia)
- 4. Tianhao Wang (University of Virginia)
- 5. Cong Shen (University of Virginia)
- 6. Jing Yang (University of Virginia)
BibTeX Citation
@inproceedings{li_sigmod26,
title = {{HeteroFedSyn: Differentially Private Tabular Data Synthesis for Heterogeneous Federated Settings}},
author = {Li, Xiaochen and Gao, Fengyu and Wei, Xizixiang and Wang, Tianhao and Shen, Cong and Yang, Jing},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802072},
url = {https://dl.acm.org/doi/10.1145/3802072},
year = {2026}
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Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,169 | PrivBayes: Private Data Release via Bayesian Networks | 2014 | SIGMOD | 0.00011838753 |
| 1,504 | A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy | 2014 | VLDB | 0.00010548904 |
| 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 |
| 3,260 | Answering Range Queries Under Local Differential Privacy | 2019 | SIGMOD | 7.589057e-05 |
| 9,303 | SPAS: Continuous Release of Data Streams under w-Event Differential Privacy | 2025 | SIGMOD | 5.289545e-05 |
| 10,345 | Benchmarking Differentially Private Tabular Data Synthesis: [Experiments & Analysis] | 2026 | SIGMOD | 5.093636e-05 |
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