Secure Multiparty Computation of a Social Network

The society today is better connected as a result of advancement in technology. The study of these social interactions and its resulting structure, is an integral component in the field of network science. However, the study of these social networks is limited to the availability of data. Privacy concerns restrict the access to network data with sensitive information. Networks that capture the relations such as trust, enmity, sexual contact, are a few examples of sensitive networks. A study of these sensitive networks is important in unraveling the behavioral aspects of the concerned individuals. The current paper proposes a multiparty computation algorithm that allows the construction of the unlabeled isomorphic version of the underlying network. The algorithm is information theoretic secure and works under the malicious adversarial model with the threshold of one third total corrupt parties.