Inferring user relationship from hidden information in WLANs

With ever increasing usage of handheld devices and vast deployment of wireless networks, we observe that it is possible to collect data from mobile devices and reveal personal relationships of their owners. In the paper, we exploit the hidden information collected from WLAN devices and infer individual relationships between device pairs based on three observation dimensions: network association history, physical proximity and spatio-temporal behavior. By measuring WLAN data, we demonstrate that device owners with social relationship tend to share access points, or show similar behavior patterns in wireless communications (e.g. go to the same place periodically to access the same WLAN network). These results can be exploited for various network analytic purposes.