We consider the problem of making graph databases such as social network structures available to researchers for knowledge discovery while providing privacy to the participating entities. We show that for a specific parametric graph model, the Kronecker graph model, one can construct an estimator of the true parameter in a way that both satisfies the rigorous requirements of differential privacy and is asymptotically efficient in the statistical sense. The estimator, which may then be published, defines a probability distribution on graphs. Sampling such a distribution yields a synthetic graph that mimics important properties of the original sensitive graph and, consequently, could be useful for knowledge discovery.
['Darakhshan J. Mir', 'Rebecca N. Wright']
A Differentially Private Graph Estimator