PrivGraph: Differentially Private Graph Data Publication by Exploiting Community Information. (arXiv:2304.02401v1 [cs.CR])

Graph data is used in a wide range of applications, while analyzing graph
data without protection is prone to privacy breach risks. To mitigate the
privacy risks, we resort to the standard technique of differential privacy to
publish a synthetic graph. However, existing differentially private graph
synthesis approaches either introduce excessive noise by directly perturbing
the adjacency matrix, or suffer significant information loss during the graph
encoding process. In this paper, we propose an effective graph synthesis
algorithm PrivGraph by …

algorithm applications breach community data differential privacy encoding exploiting information loss matrix privacy privacy breach privacy risks private process protection risks standard synthetic