How Far Removed Are You? Scalable Privacy-Preserving Estimation of Social Path Length with Social PaL

Online social networks (OSNs) are increasingly used to help users make trust and access control decisions, e.g., based on the existence and the strength of social relationships. Discovering the length of the social path between two users is often supported by popular social networks, however, in a centralized way. This requires users to always rely on the provider and, worse yet, to reveal their interest in each other. In this paper, we introduce Social Pal, the first system supporting the ubiquitous privacy-preserving discovery of the social path length between any two social network users. We prove that Social Pal allows users to find all paths of length two and demonstrate, via extensive simulation, that it discovers a significant fraction of longer paths, even with a small percentage of users in the system, e.g. 70% of all paths with only 40% of the users. Then, we implement Social Pal by means of a scalable server-side architecture and a modular Android client library, and design two mobile apps on top of it that allow nearby users to interact and share their Internet connection based on their social relationship.