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.
Differentially Private Inference via Noisy Optimization.The Annals of Statistics, 51(5):2067 – 2092, 2023
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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.