English

Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy

Data Structures and Algorithms 2019-05-28 v1 Machine Learning

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.

Keywords

Cite

@article{arxiv.1905.10477,
  title  = {Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy},
  author = {Adam Sealfon and Jonathan Ullman},
  journal= {arXiv preprint arXiv:1905.10477},
  year   = {2019}
}
R2 v1 2026-06-23T09:23:22.621Z