INVESTIGATING THE PRIVACY VERSUS 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, or share similar interests, etc. Performing “friend proximity detection” in a privacy-preserving way 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 for the first time the fundamental tradeoff between privacy preservation and application-level performance in mobile social networks. More specifically, we consider a mobile social networking application for opportunistic networks called interest-casting. In the interest-casting model, a user wants to deliver a piece of information to other users sharing similar interests (“friends”), possibly through multi-hop forwarding. 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 versus accuracy tradeoff is analyzed both theoretically, and through simulations based on a real-world mobility trace. The results of our study demonstrate for the first time that privacy preservation is at odds with forwarding accuracy, and that the best tradeoff between these two conflicting goals should be identified based on the application-level requirements