  In this paper, we formalize the notion of distributed sensitive social
networks (DSSNs), which encompasses networks like enmity networks, financial
transaction networks, supply chain networks and sexual relationship networks.
Compared to the well studied traditional social networks, DSSNs are often more
challenging to study, given the privacy concerns of the individuals on whom the
network is knit. In the current work, we envision the use of secure multiparty
tools and techniques for performing privacy preserving social network analysis
over DSSNs. As a step towards realizing this, we design efficient
data-oblivious algorithms for computing the K-shell decomposition and the
PageRank centrality measure for a given DSSN. The designed data-oblivious
algorithms can be translated into equivalent secure computation protocols. We
also list a string of challenges that are needed to be addressed, for employing
secure computation protocols as a practical solution for studying DSSNs.
