Edge differentially private estimation in the $\beta$-model via jittering and method of moments

Understand

A standing challenge in data privacy is the trade-off between the level of privacy and the efficiency of statistical inference.

• Here we conduct an in-depth study of this trade-off for parameter estimation in the $\beta$-model (Chatterjee, Diaconis and Sly, 2011) for edge differentially private network data released via jittering (Karwa, Krivitsky and Slavkovi\'{c}, 2017).
• Unlike most previous approaches based on maximum likelihood estimation for this network model, we proceed via method-of-moments.
• This choice facilitates our exploration of a substantially broader range of privacy levels - corresponding to stricter privacy - than has been to date.