Exponential random graph estimation under differential privacy

Abstract

The effective analysis of social networks and graph-structured data is often limited by the privacy concerns of individuals whose data make up these networks. Differential privacy offers individuals a rigorous and appealing guarantee of privacy. But while differentially private algorithms for computing basic graph properties have been proposed, most graph modeling tasks common in the data mining community cannot yet be carried out privately.