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For the differential privacy under the sub-Gamma noise, we derive theasymptotic properties of a class of network models with binary values with ageneral link function. In this paper, we release the degree sequences of thebinary networks under a general noisy mechanism with the discrete Laplacemechanism as a special case. We establish the asymptotic result including bothconsistency and asymptotically normality of the parameter estimator when thenumber of parameters goes to infinity in a class of network models. Simulationsand a real data example are provided to illustrate asymptotic results.

Asymptotic in a class of network models with an increasing sub-Gamma degree sequence