Robust Privacy-Preserving Triangle Counting under Edge Local Differential Privacy
Summary: Vertex-centric triangle counting under edge LDP leverages a larger portion of the noisy adjacency matrix to refine per-vertex triangle counts. Tight global-sensitivity bounds and unbiased estimators with optimized privacy-budget allocation minimize L2 loss; validated on 12 datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yizhang He (University of New South Wales)
- 2. Kai Wang (Shanghai Jiao Tong University)
- 3. Wenjie Zhang (University of New South Wales)
- 4. Xuemin Lin (Shanghai Jiao Tong University)
- 5. Ying Zhang (University of Technology Sydney)
- 6. Wei Ni (Commonwealth Scientific and Industrial Research Organisation)
BibTeX Citation
@inproceedings{he_sigmod25,
title = {{Robust Privacy-Preserving Triangle Counting under Edge Local Differential Privacy}},
author = {He, Yizhang and Wang, Kai and Zhang, Wenjie and Lin, Xuemin and Zhang, Ying and Ni, Wei},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725348},
url = {https://dl.acm.org/doi/10.1145/3725348},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,375 | Improved Lower Bounds for Privacy under Continual Release | 2026 | PODS | 4.9793485e-05 |
| 10,404 | Acyclic Graph Pattern Counting under Local Differential Privacy | 2026 | SIGMOD | 4.9793485e-05 |
| 10,581 | N2E: A General Framework to Reduce Node-Differential Privacy to Edge-Differential Privacy for Graph Analytics | 2026 | SIGMOD | 4.9793485e-05 |
| 10,631 | Defense against Poisoning Attacks under Shuffle-DP | 2026 | SIGMOD | 4.9793485e-05 |
| 10,634 | Efficient and Effective Biclique Counting with Local Differential Privacy | 2026 | SIGMOD | 4.9793485e-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 |
|---|---|---|---|---|
| 121 | Boosting the Accuracy of Differentially Private Histograms Through Consistency | 2010 | VLDB | 0.00031089378 |
| 609 | Private Analysis of Graph Structure | 2011 | VLDB | 0.00015591971 |
| 1,354 | Truss-based Community Search over Large Directed Graphs | 2020 | SIGMOD | 0.0001092812 |
| 5,673 | Mining Frequent Patterns with Differential Privacy | 2013 | VLDB | 6.0432078e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 6,963 | Fully Dynamic Algorithms for Graph Databases with Edge Differential Privacy | 2025 | PODS |
| 2 | 10,581 | N2E: A General Framework to Reduce Node-Differential Privacy to Edge-Differential Privacy for Graph Analytics | 2026 | SIGMOD |
| 3 | 11,313 | Sectric: Towards Accurate, Privacy-preserving and Efficient Triangle Counting | 2025 | VLDB |
| 4 | 10,404 | Acyclic Graph Pattern Counting under Local Differential Privacy | 2026 | SIGMOD |
| 5 | 2,009 | Private Release of Graph Statistics using Ladder Functions | 2015 | SIGMOD |
| 6 | 6,210 | Global and Local Differentially Private Release of Count-Weighted Graphs | 2023 | SIGMOD |
| 7 | 609 | Private Analysis of Graph Structure | 2011 | VLDB |
| 8 | 10,634 | Efficient and Effective Biclique Counting with Local Differential Privacy | 2026 | SIGMOD |
| 9 | 9,735 | Common Neighborhood Estimation over Bipartite Graphs under Local Differential Privacy | 2024 | SIGMOD |
| 10 | 9,729 | Practical and Accurate Local Edge Differentially Private Graph Algorithms | 2025 | VLDB |