Data-Driven Privacy Analytics: A WeChat Case Study in Location-Based Social Networks

Rongrong Wang, Minhui Xue, Kelvin Liu, Haifeng Qian

Abstract: Location-based Social Network (LBSN) services enable people to discover users nearby and establish the communication with them. WeChat as both LBSN and Online Social Network (OSN) application does not impose a real-name policy for usernames, leaving the users to choose how they want to be identified by nearby people. In this paper, we show the feasibility to stalk WeChat users in any city from any place in the world and in parallel examine the anonymity of those users. Based on previous studies, we develop an automated attacking methodology by using fake GPS location, smart phone emulation, task automation, and optical character recognition (OCR). We then study the prevalence and behavior of Anonymous and Identifiable WeChat users and correlate their anonymity with their behavior, especially for those who repeatedly query the People Nearby service, a feature that triggers WeChat to discover nearby people. By monitoring Wall Street for 7 days, we gather location information relevant to 3,215 distinct users and finally find that Anonymous users are largely less inhibited to be dynamic participants, as they query more and are more willing to move around in public. To the best of our knowledge, this is the first work that quantifies the relationship between user mobility and user anonymity. We expect our study to motivate better privacy design in WeChat.