论文标题
用于参数增加的差异私有$β$模型的渐近理论增加
Asymptotic Theory for Differentially Private Generalized $β$-models with Parameters Increasing
论文作者
论文摘要
建模边缘权重在网络数据的分析中起着至关重要的作用，这揭示了个人之间关系的程度。由于体重信息的多样性，共享这些数据已成为一种复杂的挑战。在本文中，我们考虑了非降级过程的情况，即在广义$β$模型中实现隐私和权重信息之间的权衡。在带有离散拉普拉斯机制的边缘差异隐私下，显示模型参数方程式的z遗传器被证明是一致且渐近地正态分布的。给出了模拟和真实数据示例，以进一步支持理论结果。
Modelling edge weights play a crucial role in the analysis of network data, which reveals the extent of relationships among individuals. Due to the diversity of weight information, sharing these data has become a complicated challenge in a privacy-preserving way. In this paper, we consider the case of the non-denoising process to achieve the trade-off between privacy and weight information in the generalized $β$-model. Under the edge differential privacy with a discrete Laplace mechanism, the Z-estimators from estimating equations for the model parameters are shown to be consistent and asymptotically normally distributed. The simulations and a real data example are given to further support the theoretical results.
