Fully Dynamic Algorithms for Graph Databases with Edge Differential Privacy
Summary: First differentially private, fully dynamic graph algorithms for triangle count, connected components, max matching, and degree histogram under continual edge updates. Bounds for event- and item-level DP; proves exponential dependence on time steps and, for item-level privacy, matches lower bounds for several problems. (summarized by gpt-5-nano on Feb 09 2026)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 136 | Revealing Information while Preserving Privacy | 2003 | PODS | 0.0004241101 |
| 568 | Practical Privacy: The SuLQ Framework | 2005 | PODS | 0.00019949368 |
| 1,177 | Recursive Mechanism: Towards Node Differential Privacy and Unrestricted Joins | 2013 | SIGMOD | 0.00013470212 |
| 1,602 | Calibrating Data to Sensitivity in Private Data Analysis: A Platform for Differentially-Private Analysis of Weighted Datasets | 2014 | VLDB | 0.00011199166 |
| 2,226 | Publishing Graph Degree Distribution with Node Differential Privacy | 2016 | SIGMOD | 9.2421776e-05 |
| 2,683 | Private Release of Graph Statistics using Ladder Functions | 2015 | SIGMOD | 8.315553e-05 |
| 2,894 | Pan-private Algorithms Via Statistics on Sketches | 2011 | PODS | 7.9474698e-05 |
| 8,748 | Databases as Graphs: Predictive Queries for Declarative Machine Learning | 2023 | PODS | 4.456315e-05 |
| 11,164 | Node-Differentially Private Estimation of the Number of Connected Components | 2023 | PODS | 4.1945683e-05 |
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