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Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy
Jonathan Ullman · Adam Sealfon

Tue Dec 10 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #91

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 in a small interval.

Author Information

Jonathan Ullman (Northeastern University)
Adam Sealfon (UC Berkeley)

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