A differentially private estimator for the stochastic Kronecker graph model
Darakhshan Mir, Rebecca N. Wright · 2012
We consider the problem of making graph databases such as social networks available to researchers for knowledge discovery while providing privacy to the participating entities. We use a parametric graph model, the stochastic Kronecker graph model, to model the observed graph and construct an estimator of the "true parameter" in a way that both satisfies the rigorous requirements of differential privacy and demonstrates experimental utility on several important graph statistics. 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.