Differentially Private Synthesis and Sharing of Network Data Via Bayesian Exponential Random Graph Models

Liu, Fang; Eugenio, Evercita C.; Jin, Ick Hoon; Bowen, Claire Mckay

doi:10.1093/jssam/smac017

Differentially Private Synthesis and Sharing of Network Data Via Bayesian Exponential Random Graph Models

Journal Article · Wed Jun 15 00:00:00 EDT 2022 · Journal of Survey Statistics and Methodology

DOI:https://doi.org/10.1093/jssam/smac017· OSTI ID:1872498

; Eugenio, Evercita C.; Jin, Ick Hoon; Bowen, Claire Mckay

Abstract

Network data often contain sensitive relational information. One approach to protecting sensitive information while offering flexibility for network analysis is to share synthesized networks based on the information in originally observed networks. We employ differential privacy (DP) and exponential random graph models (ERGMs) and propose the DP-ERGM method to synthesize network data. We apply DP-ERGM to two real-world networks. We then compare the utility of synthesized networks generated by DP-ERGM, the DyadWise Randomized Response (DWRR) approach, and the Synthesis through Conditional distribution of Edge given nodal Attribute (SCEA) approach. In general, the results suggest that DP-ERGM preserves the original information significantly better than two other approaches in network structural statistics and inference for ERGMs and latent space models. Furthermore, DP-ERGM satisfies node DP through modeling the global network structure with ERGM, a stronger notion of privacy than the edge DP under which DWRR and SCEA operate.

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Sponsoring Organization:: USDOE

OSTI ID:: 1872498

Alternate ID(s):: OSTI ID: 1883182

Journal Information:: Journal of Survey Statistics and Methodology, Journal Name: Journal of Survey Statistics and Methodology Journal Issue: 3 Vol. 10; ISSN 2325-0984

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United States

Language:: English

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