The goal of this research project is to enable statistical analysis and knowledge discovery on networks without violating the privacy of participating entities. Network data sets record the structure of computer, communication, social, or organizational networks, but they often contain highly sensitive information about individuals. The availability of network data is crucial for analyzing, modeling, and predicting the behavior of networks. The team's approach is based on model-based generation of synthetic data, in which a model of the network is released under strong privacy conditions and samples from that model are studied directly by analysts. Output perturbation techniques are used to privately compute the parameters of popular network models. The resulting "noisy" model parameters are released, satisfying a strong, quantifiable privacy guarantee, but still preserving key properties of the networks. Analysts can use the released models to sample individual networks or to reason about properties of the implied ensemble of networks. By synthesizing versions of networks that would otherwise remain hidden, this research can advance the study of topics such as disease transmission, network resiliency, and fraud detection. The project will result in publicly available privacy tools, a repository for derived models and sample networks, and contributions to workforce development in the field of information assurance. The experimental research is linked to educational efforts including undergraduate involvement in research through a Research Experience for Undergraduates site, as well as interdisciplinary seminars.
TC: Medium: Dissemination and Analysis of Private Network Data