Secure computation of social network measures

Abstract

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 this dissertation, we draw the reader’s attention to a class of social networks which are investigated to a limited extent. These networks capture interactions that are regarded as private information. More specifically, it constitutes 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 networks, advice networks, and enmity networks 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. Employing various optimizations, including oblivious data structures and oblivious RAM, we present secure variants of some of the commonly used social network analysis techniques. We evaluate the performance of the designed protocols by implementing them in the Obliv-C framework for secure computation. We benchmark their performance using the state of the art oblivious RAM schemes as well.