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

翻译：对于亚伽马噪音下的差分隐私,我们得出了具有一般链接功能的二进制值网络模型的无症状特性。在本文中,我们将二进制网络的分级序列放入一般的噪音机制下,作为特例使用离散 Laplace 机制。当参数数移到网络模型类别无穷无尽时,我们建立了参数估计符的无症状结果,包括一致性和无症状常性。提供了模拟和真实数据实例,以说明无损效果。