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

arXiv: https://arxiv.org/abs/2002.12733 · HTML: https://mlsanity.com/paper/2002.12733/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [Asymptotic in a class of network models with an increasing sub-Gamma degree sequence](https://mlsanity.com/paper/2111.01301/) (2021)

## Abstract (arXiv)

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.