Differential Privacy for Weighted Network Based on Probability Model

Weighted network contains a lot of sensitive information and may seriously jeopardize individual privacy. In this paper, we study the problem of differential privacy for weighted network. We found most existing methods add noise to edge weights directly and neglect the structural role of node. These methods perform with low accuracy. To address the above issue, we propose two approaches. One approach describes a differential privacy method for Stochastic Block Model. This private SBM reveals and the structural role of node and respects the privacy of it. Another approach develops a differential privacy method for weighted network through structuring a private probability model. We use Variational Bayes to learn the private model parameters. It adds noise to the parameters of the probability model instead of edge weights, and achieve high data utility. Experiments on real datasets illustrate that our algorithm privately releases weighted network and achieves high accuracy.