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Title: Differentially Private Generation of Social Networks via Exponential Random Graph Models
Many social networks contain sensitive relational information. One approach to protect the sensitive relational information while offering flexibility for social network research and analysis is to release synthetic social networks at a pre-specified privacy risk level, given the original observed network. We propose the DP-ERGM procedure that synthesizes networks that satisfy the differential privacy (DP) via the exponential random graph model (EGRM). We apply DP-ERGM to a college student friendship network and compare its original network information preservation in the generated private networks with two other approaches: differentially private DyadWise Randomized Response (DWRR) and Sanitization of the Conditional probability of Edge given Attribute classes (SCEA). The results suggest that DP-EGRM preserves the original information significantly better than DWRR and SCEA in both network statistics and inferences from ERGMs and latent space models. In addition, DP-ERGM satisfies the node DP, a stronger notion of privacy than the edge DP that DWRR and SCEA satisfy.
Authors:
; ; ;
Award ID(s):
1717417 1546373
Publication Date:
NSF-PAR ID:
10187174
Journal Name:
2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)
Sponsoring Org:
National Science Foundation
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