UnLinked: Private Proximity-based Off-line OSN Interaction.

The recent decade has witnessed a rapid increase in popularity of mobile personal devices (notably, smartphones) t hat function as all-purpose personal communication portals. Concurrently, On-line Social Networks (OSNs) have continued their impressive proliferation. Meanwhile, the notion of "OSN privacy" remains elusive and even self-contradictory. Centralized nature of prominent OSNs is unlikely to change, which does not bode well for OSN users' privacy. However, some user privacy can be gained from making certain OSN functionality available off-line, such as discovering common contacts and other features, as well as establishing affinity- based connections. OSN providers stand to gain from this, since users could avail themselves of OSN functionality in scenarios where none currently exists, e.g., whenever Internet connectivity is unavailable, expensive or insufficient. At the same time, OSN users benefit from increased privacy because off-line interactions can be made opaque to OSN providers. This paper explores off-line private proximity-based use of OSNs. Although our approach is quite general, the proposed system (called UnLinked) is grafted atop a specific and popular OSN -- LinkedIn. One key challenge is how to ensure authenticity and privacy of users' information (e.g., connections and other profile data) when they engage in off-line interactions. This is addressed by designing an efficient technique for authorized two-way private set intersection (ATW-PSI), which allows two OSN users to jointly learn only the intersection of their input sets, while being assured of the authenticity of each others' input. The paper describes and evaluates a practical prototype that allows physically proximate LinkedIn users to commit to a connection if they have a mutually acceptable number of common connections.