MPC meets SNA: A Privacy Preserving Analysis of Distributed Sensitive Social Networks

TL;DR

This paper introduces a framework for privacy-preserving analysis of distributed sensitive social networks using secure multiparty computation, focusing on algorithms for K-shell and PageRank centrality.

Contribution

It formalizes distributed sensitive social networks and develops efficient data-oblivious algorithms for key network analysis measures, enabling privacy-preserving computations.

Findings

Designed data-oblivious algorithms for K-shell and PageRank.

Algorithms can be translated into secure computation protocols.

Identified challenges for practical deployment of secure protocols.

Abstract

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.