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The Cartographer
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy
May 24, 2019 Β· Declared Dead Β· π Neural Information Processing Systems
"No code URL or promise found in abstract"
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Authors
Adam Sealfon, Jonathan Ullman
arXiv ID
1905.10477
Category
cs.DS: Data Structures & Algorithms
Cross-listed
cs.LG
Citations
48
Venue
Neural Information Processing Systems
Last Checked
3 months ago
Abstract
We give a simple, computationally efficient, and node-differentially-private algorithm for estimating the parameter of an Erdos-Renyi graph---that is, estimating p in a G(n,p)---with near-optimal accuracy. Our algorithm nearly matches the information-theoretically optimal exponential-time algorithm for the same problem due to Borgs et al. (FOCS 2018). More generally, we give an optimal, computationally efficient, private algorithm for estimating the edge-density of any graph whose degree distribution is concentrated on a small interval.
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