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.
|Title of host publication||Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020|
|Editors||W. K. Chan, Bill Claycomb, Hiroki Takakura, Ji-Jiang Yang, Yuuichi Teranishi, Dave Towey, Sergio Segura, Hossain Shahriar, Sorel Reisman, Sheikh Iqbal Ahamed|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2020 Jul|
|Event||44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020 - Virtual, Madrid, Spain|
Duration: 2020 Jul 13 → 2020 Jul 17
|Name||Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020|
|Conference||44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020|
|Period||20/7/13 → 20/7/17|
Bibliographical noteFunding Information:
Fang Liu and Evercita C. Eugenio are/were supported by the National Science Foundation (NSF) Grants #1546373 and #1717417. Ick Hoon Jin was partially supported by the Yonsei University Research Fund of 2019-22-0210 and National Research Foundation of Korea (NRF 2020R1A2C1A01009881), and Claire Bowen was supported by the NSF Graduate Research Fellowship # DGE-1313583 during part of the paper’s development. The publication has been assigned the Sandia National Laboratories identifier SAND2020-5256 C. We thank Dr. Johnny Zhiyong Zhang from the Lab for Big Data Methodology in the Department of Psychology at the University of Notre Dame for providing the Chinese college friendship data.
© 2020 IEEE.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture