Poster
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy
Jonathan Ullman · Adam Sealfon
East Exhibition Hall B, C #91
Keywords: [ Learning Theory ] [ Theory ] [ Applications ] [ Privacy, Anonymity, and Security ]
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.
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