Many data mining and analytical tasks rely on the abstraction of networks
(graphs) to summarize relational structures among individuals (nodes). Since
relational data are often sensitive, we aim to seek effective approaches to
generate utility-preserved yet privacy-protected structured data. In this
paper, we leverage the differential privacy (DP) framework to formulate and
enforce rigorous privacy constraints on deep graph generation models, with a
focus on edge-DP to guarantee individual link privacy. In particular, we
enforce edge-DP by injecting proper noise to the gradients of a link
reconstruction-based graph generation model, while ensuring data utility by
improving structure learning with structure-oriented graph discrimination.
Extensive experiments on two real-world network datasets show that our proposed
DPGGAN model is able to generate graphs with effectively preserved global
structure and rigorously protected individual link privacy.

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