Private social network analysis

Connections in distributed systems, such as social networks, online communities or peer-to-peer networks, form complex graphs. These graphs are of interest to scientists in fields as varied as marketing, epidemiology and psychology. However, knowledge of the graph is typically distributed among a large number of subjects, each of whom knows only a small piece of the graph. Efforts to assemble these pieces often fail because of privacy concerns: subjects refuse to share their local knowledge of the graph. To assuage these privacy concerns, we propose reconstructing the whole graph privately, i.e., in a way that hides the correspondence between the nodes and edges in the graph and the real-life entities and relationships that they represent. We first model the privacy threats posed by the private reconstruction of a distributed graph. Our model takes into account the possibility that malicious nodes may report incorrect information about the graph in order to facilitate later attempts to de-anonymize the reconstructed graph. We then propose protocols to privately assemble the pieces of a graph in ways that mitigate these threats. These protocols severely restrict the ability of adversaries to compromise the privacy of honest subjects.