Differentially Private Synthesis and Sharing of Network Data Via Bayesian Exponential Random Graph Models Article Swipe
Fang Liu
,
Evercita C. Eugenio
,
Ick Hoon Jin
,
Claire McKay Bowen
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/jssam/smac017
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/jssam/smac017
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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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/jssam/smac017
- OA Status
- green
- Cited By
- 3
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4225818099
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4225818099Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/jssam/smac017Digital Object Identifier
- Title
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Differentially Private Synthesis and Sharing of Network Data Via Bayesian Exponential Random Graph ModelsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-03-29Full publication date if available
- Authors
-
Fang Liu, Evercita C. Eugenio, Ick Hoon Jin, Claire McKay BowenList of authors in order
- Landing page
-
https://doi.org/10.1093/jssam/smac017Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.osti.gov/biblio/1872498Direct OA link when available
- Concepts
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Exponential random graph models, Inference, Computer science, Statistical inference, Exponential family, Bayesian network, Graph, Data mining, Theoretical computer science, Random graph, Artificial intelligence, Mathematics, Machine learning, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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45Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Journal of Survey Statistics and Methodology |
| primary_location.landing_page_url | https://doi.org/10.1093/jssam/smac017 |
| publication_date | 2022-03-29 |
| publication_year | 2022 |
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