"SecGCM: Secure Group Centrality Maximization on Graphs with Secure Multi-party Computation"
Abstract
"We provide the preprocessed graph datasets used in SecGCM experiments, containing graphs with 34, 217, 332, 379, 516, and 1174 nodes respectively. These synthetic or extracted networks simulate distributed multi-party scenarios for evaluating privacy-preserving group closeness centrality maximization via secure multi-party computation."
Fields of Study & Subjects
Privacy-Preserving Technologies in Data
Cryptography and Data Security
Cloud Data Security Solutions
Publication details
- Journal / source
- IEEE DataPort
- Publication date
- 13 January 2026
- Document type
- Dataset