Affiliation network is one kind of two-mode social network with two different sets of nodes (namely, a set of actors and a set of social events) and edges representing the affiliation of the actors with the social events. The asymptotic theorem of a differentially private estimator of the parameter in the private \(p_{0}\) model has been established. However, the \(p_{0}\) model only focuses on binary edges for one-mode network. In many case, the connections in many affiliation networks (two-mode) could be weighted, taking a set of finite discrete values. In this paper, we derive the consistency and asymptotic normality of the moment estimators of parameters in affiliation finite discrete weighted networks with a differentially private degree sequence. Simulation studies and a real data example demonstrate our theoretical results.

归属网络是一种具有两组不同节点（即一组参与者和一组社会事件）和代表参与者与社会事件的从属关系的边的双模式社交网络。建立了私有\(p_{0}\)模型中参数的微分私有估计量的渐近定理。然而，\(p_{0}\)模型只关注单模网络的二元边。在许多情况下，可以对许多附属网络（双模式）中的连接进行加权，采用一组有限的离散值。在本文中，我们推导出具有差分私有度序列的从属有限离散加权网络中参数矩估计量的一致性和渐近正态性。模拟研究和真实数据示例证明了我们的理论结果。