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In this paper, we formalize the notion of distributed sensitive socialnetworks (DSSNs), which encompasses networks like enmity networks, financialtransaction networks, supply chain networks and sexual relationship networks.Compared to the well studied traditional social networks, DSSNs are often morechallenging to study, given the privacy concerns of the individuals on whom thenetwork is knit. In the current work, we envision the use of secure multipartytools and techniques for performing privacy preserving social network analysisover DSSNs. As a step towards realizing this, we design efficientdata-oblivious algorithms for computing the K-shell decomposition and thePageRank centrality measure for a given DSSN. The designed data-obliviousalgorithms can be translated into equivalent secure computation protocols. Wealso list a string of challenges that are needed to be addressed, for employingsecure computation protocols as a practical solution for studying DSSNs.

MPC meets SNA: A Privacy Preserving Analysis of Distributed Sensitive Social Networks