The work introduces edge-DP algorithms that privatize sufficient statistics for power-law exponent estimation in graphs, enabling both centralized and local models with evaluations across privacy budgets and datasets.
A Two-Phase Algorithm for Generating Synthetic Graph Under Local Differential Privacy
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Estimating Power-Law Exponent with Edge Differential Privacy
The work introduces edge-DP algorithms that privatize sufficient statistics for power-law exponent estimation in graphs, enabling both centralized and local models with evaluations across privacy budgets and datasets.