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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)

Paper ID
7326
Venue
SIGMOD
Year
2025
Pagerank
5.8981989e-05
Overall Rank
6,367 | 56.32%
DOI
10.1145/3725348

Incoming Non-self Citations Over Time

Authors

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)

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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 0.00031639377
596 Private Analysis of Graph Structure 2011 VLDB 0.00015926024
1,328 Truss-based Community Search over Large Directed Graphs 2020 SIGMOD 0.00011140898
5,538 Mining Frequent Patterns with Differential Privacy 2013 VLDB 6.1819133e-05
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