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

Fang Liu, Evercita C. Eugenio, Ick Hoon Jin, Claire Mc Kay Bowen

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)753-784
Number of pages32
JournalJournal of Survey Statistics and Methodology
Volume10
Issue number3
DOIs
Publication statusPublished - 2022 Jun 1

Bibliographical note

Funding Information:
Fang Liu and Evercita Eugenio were supported by the National Sicence Foundation [IIS-1546373]

Publisher Copyright:
© 2022 The Author(s) 2022. Published by Oxford University Press on behalf of the American Association for Public Opinion Research. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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