Abstract

I’ll present two algorithms called MobID¹ and FriendSensing². Using short-range technologies (e.g., Bluetooth) on their mobile phones, users keep track of other phones in their proximity. Upon proximity records, MobID identifies Sybil attackers in a decentralized way, and FriendSensing recommends social contacts:

- The idea behind MobID is that a device manages two small networks in which it stores information about the devices it meets: its network of friends contains honest devices, and its network of foes contains suspicious devices. By reasoning on these two networks, the device is then able to determine whether an unknown individual is carrying out a Sybil attack or not.

- FriendSensing processes proximity records using a variety of algorithms that are based on social network theories of geographical proximity and of link prediction. It then returns a personalized and automatically generated list of people the user may know. We’ll see how both algorithms perform against real mobility and social network data.

[1] Sybil Attacks Against Mobile Users: Friends and Foes to the Rescue. Infocom ‘10

[2] FriendSensing: Recommending Friends Using Mobile Phones. RecSys ‘09