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.
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References
Xue, M., Liu, Y., Ross, K.W., Qian, H.: I know where you are: thwarting privacy protection in location-based social discovery services. In: 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE (2015)
Li, M., Zhu, H., Gao, Z., Chen, S., Yu, L., Hu, S., Ren, K.: All your location are belong to us: breaking mobile social networks for automated user location tracking. In: Proceedings of the 15th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 43–52. ACM (2014)
Peddinti, S.T., Ross, K.W., Cappos, J.: On the internet, nobody knows you’re a dog: a twitter case study of anonymity in social networks. In: Proceedings of the Second Edition of the ACM Conference on Online Social Networks, pp. 83–94. ACM (2014)
Ding, Y., Peddinti, S.T., Ross, K.W.: Stalking beijing from timbuktu: a generic measurement approach for exploiting location-based social discovery. In: Proceedings of the 4th ACM Workshop on Security and Privacy in Smartphones & Mobile Devices, pp. 75–80. ACM (2014)
Chen, T., Kaafar, M.A., Boreli, R.: The where and when of finding new friends: analysis of a location-based social discovery network. In: ICWSM (2013)
Srivatsa, M., Hicks, M.: Deanonymizing mobility traces: using social network as a side-channel. In: Proceedings of the 2012 ACM Conference on Computer and Communications Security, pp. 628–637. ACM (2012)
Zang, H., Bolot, J.: Anonymization of location data does not work: a large-scale measurement study. In: Proceedings of the 17th Annual International Conference on Mobile Computing and Networking, pp. 145–156. ACM (2011)
de Montjoye, Y.-A., Hidalgo, C.A., Verleysen, M., Blondel, V.D.: Unique in the crowd: the privacy bounds of human mobility. Sci. Rep. 3, 1–5 (2013)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China, under Grant 61172085 and 61021004, in part by the Science and Technology Commission of Shanghai Municipality under Grant 13JC1403500.
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Wang, R., Xue, M., Liu, K., Qian, H. (2015). Data-Driven Privacy Analytics: A WeChat Case Study in Location-Based Social Networks. In: Xu, K., Zhu, H. (eds) Wireless Algorithms, Systems, and Applications. WASA 2015. Lecture Notes in Computer Science(), vol 9204. Springer, Cham. https://doi.org/10.1007/978-3-319-21837-3_55
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DOI: https://doi.org/10.1007/978-3-319-21837-3_55
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