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Differential Privacy for Weighted Network Based on Probability Model
2020
IEEE Access
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
doi:10.1109/access.2020.2991062
fatcat:bzigvbwwmja3dhlq7cywtbykum