Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
LORA: link obfuscation by randomization in graphs
SDM'11 Proceedings of the 8th VLDB international conference on Secure data management
Sharing graphs using differentially private graph models
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
A differentially private estimator for the stochastic Kronecker graph model
Proceedings of the 2012 Joint EDBT/ICDT Workshops
A Guide to Differential Privacy Theory in Social Network Analysis
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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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.