Practical and Accurate Local Edge Differentially Private Graph Algorithms
Summary: Introduces practical local-DP algorithms for k-core decomposition and triangle counting, with utility governed by private degeneracy/max degree rather than edge count. Distributed experiments show near-exact k-cores and up to six-order-of-magnitude lower triangle-counting error. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Pranay Mundra (Yale University)
- 2. Charalampos Papamanthou (Yale University)
- 3. Julian Shun (Massachusetts Institute of Technology)
- 4. Quanquan C. Liu (Yale University)
BibTeX Citation
@article{mundra_vldb25,
title = {{Practical and Accurate Local Edge Differentially Private Graph Algorithms}},
author = {Mundra, Pranay and Papamanthou, Charalampos and Shun, Julian and Liu, Quanquan C.},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {11},
pages = {4199--4213},
doi = {10.14778/3749646.3749687},
url = {https://doi.org/10.14778/3749646.3749687},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,158 | Improved Lower Bounds for Privacy under Continual Release | 2026 | PODS | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 218 | Limiting Privacy Breaches in Privacy Preserving Data Mining | 2003 | PODS | 0.00024420564 |
| 2,279 | Answering Multi-Dimensional Analytical Queries under Local Differential Privacy | 2019 | SIGMOD | 8.8155688e-05 |
| 3,260 | Answering Range Queries Under Local Differential Privacy | 2019 | SIGMOD | 7.589057e-05 |
| 4,358 | Parallel Index-Based Structural Graph Clustering and Its Approximation | 2021 | SIGMOD | 6.7459836e-05 |
| 6,083 | Global and Local Differentially Private Release of Count-Weighted Graphs | 2023 | SIGMOD | 5.9833455e-05 |
| 8,198 | Local Dampening: Differential Privacy for Non-numeric Queries via Local Sensitivity | 2021 | VLDB | 5.4688368e-05 |
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| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 1,960 | Private Release of Graph Statistics using Ladder Functions | 2015 | SIGMOD |
| 2 | 10,385 | N2E: A General Framework to Reduce Node-Differential Privacy to Edge-Differential Privacy for Graph Analytics | 2026 | SIGMOD |
| 3 | 10,446 | Efficient and Effective Biclique Counting with Local Differential Privacy | 2026 | SIGMOD |
| 4 | 9,557 | Common Neighborhood Estimation over Bipartite Graphs under Local Differential Privacy | 2024 | SIGMOD |
| 5 | 10,921 | Sectric: Towards Accurate, Privacy-preserving and Efficient Triangle Counting | 2025 | VLDB |
| 6 | 6,826 | Fully Dynamic Algorithms for Graph Databases with Edge Differential Privacy | 2025 | PODS |
| 7 | 6,083 | Global and Local Differentially Private Release of Count-Weighted Graphs | 2023 | SIGMOD |
| 8 | 596 | Private Analysis of Graph Structure | 2011 | VLDB |
| 9 | 10,188 | Acyclic Graph Pattern Counting under Local Differential Privacy | 2026 | SIGMOD |
| 10 | 6,367 | Robust Privacy-Preserving Triangle Counting under Edge Local Differential Privacy | 2025 | SIGMOD |