Abstract:
The advancement in technology has resulted in a better connected society. These connections foster social interactions
that result in an emergent structure. This structure is popularly termed as a social network and is an integral component
of study, in the field of network science. However, the study
of these social networks is limited to the availability of data
on the underlying social interactions. 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 an unlabeled random isomorphic version of a distributedly held network. The protocol
is proven to be secure in the presence of the extended arithmetic black-box, which supports the operations of addition,
multiplication, comparison and equality checks.
