MultiCent: Secure and Scalable Computation of Centrality Measures on Multilayer Graphs

As real-world networks such as social networks and computer networks are often complex and distributed, modeling them as multilayer graphs is gaining popularity. For instance, when studying social interactions across platforms like LinkedIn, Facebook, TikTok, and Bluesky, users may be connected on several of these platforms. To identify important nodes/users, the platforms might wish to analyze user interactions using, e.g., centrality measures when accounting for connections across all platforms. This raises the challenge for platforms to perform such computation while simultaneously protecting their user data to shelter their own business as well as uphold data protection laws. Hence, it necessitates designing solutions that allow for performing secure computation on a multilayer graph which is distributed among mutually distrusting parties while keeping each party's data hidden. The work of Asharov et al. (WWW'17) addresses this problem by designing secure solutions for centrality measures that involve computing the truncated Katz score and reach score on multilayer graphs. However, we identify several limitations in that work which render the solution inefficient or even unfeasible for realistic networks with significantly more than 10k nodes. We address these limitations by designing secure solutions that are significantly more efficient and scalable. In more detail, given that real-world graphs are known to be sparse, our solutions move away from an expensive matrix-based representation to a more efficient list-based representation. We design novel, secure, and efficient solutions for computing centrality measures and prove their correctness. Our solutions drastically reduce the asymptotic complexity from the prohibitive O(|V|^2) even for the fastest solution by Asharov et al. down to O(|V| log |V|), for |V| nodes. To design our solutions, we extend upon the secure graph computation framework of Koti et al. (CCS'24), providing a novel framework with improved capabilities in multiple directions. Finally, we provide an end-to-end implementation of our secure graph analysis framework and establish concrete efficiency improvements over prior work, observing several orders of magnitude improvement.