Correlation-Aware Secure Sorting and Permutation for Iterative Two-Party Graph Analysis

Secure multi-party computation techniques enable in-depth analysis on joint graphs that inherently encompass comprehensive topology information and extended attributes, while preserving data privacy. In the two-party setting, existing approaches suffer from inefficiencies due to redundant secure sorting or costly secure shuffling operations required for secure message passing. Some works improve efficiency by relaxing security assumptions, either through differential privacy or by introducing helper parties.