Investigating the Privacy vs. Forwarding Accuracy Tradeoff in Opportunistic Interest-Casting
Many mobile social networking applications are based on a "friend proximity detection" step, according to which two mobile users try to jointly estimate whether they have friends in common. Preserving privacy while performing "friend proximity detection" is fundamental to achieve widespread acceptance of mobile social networking applications. However, the need of privacy preservation is often at odds with application-level performance of the mobile social networking application, since only obfuscated information about the other user's profile is available for optimizing performance. In this paper, we study the fundamental tradeoff between privacy preservation and application-level performance in mobile social networks. We consider a mobile social networking application for opportunistic networks called interest-casting, where a user wants to deliver a piece of information to other users sharing similar interests ("friends"). In this paper, we propose a privacy-preserving friend proximity detection scheme based on a protocol for solving the Yao's "Millionaire's Problem", and we introduce three interest-casting protocols achieving different tradeoffs between privacy and accuracy of the information forwarding process. The privacy vs. accuracy tradeoff is analyzed both theoretically and through simulations. Our results demonstrate that privacy preservation is at odds with forwarding accuracy, and that the best tradeoff should be identified based on the application-level requirements.