Privacy-Preserving Group Closeness Maximization

Zihao Zhao, Sijia Cao, Hanlin Zhang, Jie Lin, Fanyu Kong, Leyun Yu

Abstract: This study explores the metric of group closeness centrality within the framework of social networks, a departure from the traditional analysis focused solely on the significance of individual nodes. Given the intricate dynamics observed in networks governed by various stakeholders, we introduce a framework that preserves privacy through the application of a greedy algorithm. This approach is designed to evaluate the collective influence of groups while ensuring the confidentiality of individual data. Furthermore, we employ Oblivious Random Access Memory (ORAM) [1] within cloud servers to conceal access patterns, thereby enhancing data privacy. Through comprehensive experimentation across three real-world social network datasets within the MP-SPDZ framework [2], dedicated to secure multi-party computation, we demonstrate the efficiency of our proposed methods.