Practical and Accurate Local Edge Differentially Private Graph Algorithms
Summary: Novel LDP algorithms for k-core decomposition and triangle counting with error bounds depending on maximum degree and degeneracy (via a private out-degree orientation) instead of total edge count. New analysis and distributed-simulation evaluation show dramatic accuracy gains over prior LDP methods (k-core errors ≈3× vs 131×; triangle estimates improved by up to six orders of magnitude). (summarized by gpt-5-mini on Feb 09 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 177 | Limiting Privacy Breaches in Privacy Preserving Data Mining | 2003 | PODS | 0.00037858416 |
| 2,556 | Answering Multi-Dimensional Analytical Queries under Local Differential Privacy | 2019 | SIGMOD | 8.5485513e-05 |
| 3,073 | Answering Range Queries Under Local Differential Privacy | 2019 | SIGMOD | 7.6098046e-05 |
| 4,488 | Parallel Index-Based Structural Graph Clustering and Its Approximation | 2021 | SIGMOD | 6.139905e-05 |
| 6,231 | Global and Local Differentially Private Release of Count-Weighted Graphs | 2023 | SIGMOD | 5.1402278e-05 |
| 7,946 | Local Dampening: Differential Privacy for Non-numeric Queries via Local Sensitivity | 2021 | VLDB | 4.6089395e-05 |
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