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arXiv · 2002.12733

Asymptotic Theory for Differentially Private Generalized $\beta$-models with Parameters Increasing

Yifan Fan·Huiming Zhang·Ting Yan
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Abstract

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 $\beta$-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.

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Yifan Fan, Huiming Zhang, Ting Yan. 2020-02-28. Asymptotic Theory for Differentially Private Generalized $\beta$-models with Parameters Increasing. https://doi.org/10.4310/sii.2020.v13.n3.a8

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