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: 7264
Venue: SIGMOD
Year: 2025
Pagerank: 4.5535352e-05
Overall Rank: 8,234 | 42.72%
DOI: 10.1145/3725348

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,094	N2E: A General Framework to Reduce Node-Differential Privacy to Edge-Differential Privacy for Graph Analytics	2026	SIGMOD	4.1945683e-05
10,153	Defense against Poisoning Attacks under Shuffle-DP	2026	SIGMOD	4.1945683e-05
10,157	Efficient and Effective Biclique Counting with Local Differential Privacy	2026	SIGMOD	4.1945683e-05

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
178	Boosting the Accuracy of Differentially Private Histograms Through Consistency	2010	VLDB	0.00037697111
642	Private Analysis of Graph Structure	2011	VLDB	0.00018755196
1,637	Truss-based Community Search over Large Directed Graphs	2020	SIGMOD	0.0001105259
5,772	Mining Frequent Patterns with Differential Privacy	2013	VLDB	5.3322378e-05

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