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Asymptotic Theory for Differentially Private Generalized β-models with Parameters Increasing

02/28/2020

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by Yifan Fan, et al.

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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.

Yifan Fan
4 publications
Huiming Zhang
19 publications
Ting Yan
6 publications

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