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.
AsgLDP: Collecting and Generating Decentralized Attributed Graphs With Local Differential Privacy.IEEE Transactions on Information F orensics and Security, 15:3239–3254, 2020
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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.