Privacy Preserving Distributed Analysis of Social Networks

Social networks have been a popular choice of study, given the surge of online data on friendship networks, communication networks, collaboration networks etc. This popularity, however, is not true for all types of social networks. In the current work, we draw the reader's attention to a class of social networks which are investigated to a limited extent, classified as distributed sensitive social networks. It constitutes of networks where the presence or absence of edges in the network is distributedly known to a set of parties, who regard this information as their private data. Supply chain networks, informal networks such as trust network, advice network, enmity network, etc. are a few examples of the same. A major reason for the lack of any substantial study on these networks has been the unavailability of data. As a solution, we propose a privacy preserving approach to investigating these networks. We show the feasibility of using secure multiparty computation techniques to perform the required analysis, while preserving the privacy of every individual's data. The possible approaches that can be considered to ensure the design of efficient secure protocols are discussed such as efficient circuit design, ORAM based secure computation, use of oblivious data structures, etc. The results obtained in the direction of secure network analysis algorithms are also presented.