Abstract
The accuracy of existing geolocation methods for WeChat users depends on the stable correspondence between reported distance and actual distance. In view of the difficulty to pinpoint users’ location in real-world due to WeChat location protection strategy, a WeChat User geolocating Algorithm based on reported and actual distance relation analysis is proposed. Firstly, statistical characteristics of the relation between reported distance and actual distance are obtained based on collected data. Secondly, optimization parameters are selected based on these characteristics to determine the space where the target user is located. Finally, stepwise strategies are taken to improve the accuracy rate of space partition. Experimental results show that, on the premise that target users can be discovered, the proposed algorithm could achieve higher accuracy compared with the classical space partition based algorithm and the heuristic number theory based algorithm. The highest geolocating accuracy is within 10 m and 56% of geolocation results are within 60 m.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the International Conference on World Wide Web, pp. 591–600. ACM, Raleigh (2010)
Nemelka, C.L., Ballard, C.L., Liu, K., Xue, M., Ross, K.W.: You can yak but you can’t hide. In: Proceedings of the ACM Conference on Online Social Networks, p. 99. ACM, Stanford (2015)
Wang, G., Wang, B., Wang, T., Nika, A., Zheng, H., Zhao, B.Y.: Whispers in the dark: analysis of an anonymous social network. In: Proceedings of the Internet Measurement Conference, pp. 137–150. ACM, Vancouver (2014)
Zheng, Y.: Location-based social networks: users. In: Zheng, Y., Zhou, X. (eds.) Computing with Spatial Trajectories, pp. 243–276. Springer, New York (2011). doi:10.1007/978-1-4614-1629-6_8
Hoang, N.P., Asano, Y., Yoshikawa, M.: Your neighbors are my spies: location and other privacy concerns in dating apps. In: Proceedings of the 18th International Conference on Advanced Communication Technology, pp. 715–721. IEEE, PyeongChang (2016)
Shokri, R., Theodorakopoulos, G., Papadimitratos, P., Kazemi, E., Hubaux, J.: Hiding in the mobile crowd: location privacy through collaboration. IEEE Trans. Dependable Secure Comput. 11(3), 266–279 (2014)
Xue, M., Liu, Y., Ross, K.W., Qian, H.: I know where you are: thwarting privacy protection in location-based social discovery services. In: Proceedings of the IEEE Conference on Computer Communications Workshops, pp. 179–184. IEEE, Hong Kong (2015)
Peng, J., Meng, Y., Xue, M., Hei, X., Ross, K.W.: Attacks and defenses in location-based social networks: a heuristic number theory approach. In: Proceedings of the International Symposium on Security and Privacy in Social Networks and Big Data, pp. 64–71. IEEE, Hangzhou (2015)
Cheng, H., Mao, S., Xue, M., Hei, X.: On the impact of location errors on localization attacks in location-based social network services. In: Wang, G., Ray, I., Alcaraz Calero, J.M., Thampi, S.M. (eds.) SpaCCS 2016. LNCS, vol. 10066, pp. 343–357. Springer, Cham (2016). doi:10.1007/978-3-319-49148-6_29
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, Scottsdale (2014)
Li, M., Zhu, H., Gao, Z., Chen, S., Ren, K., Yu, L., Hu, S.: 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, Philadelphia (2014)
Polakis, I., Argyros, G., Petsios, T., Sivakorn, S., Keromytis, A.D.: Where’s Wally? Precise user discovery attacks in location proximity services. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 817–828. ACM, Denver (2015)
Wang, R., Xue, M., Liu, K., Qian, H.: Data-driven privacy analytics: a WeChat case study in location-based social networks. In: Xu, K., Zhu, H. (eds.) WASA 2015. LNCS, vol. 9204, pp. 561–570. Springer, Cham (2015). doi:10.1007/978-3-319-21837-3_55
Acknowledgment
The work presented in this paper is supported by the National Natural Science Foundation of China (No. U1636219, 61379151, 61401512, 61572052), the National Key R&D Program of China (No. 2016YFB0801303, 2016QY01W0105) and the Key Technologies R&D Program of Henan Province (No. 162102210032).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Shi, W., Luo, X., Zhao, F., Peng, Z., Gan, Y. (2017). A WeChat User Geolocating Algorithm Based on the Relation Between Reported and Actual Distance. In: Wen, S., Wu, W., Castiglione, A. (eds) Cyberspace Safety and Security. CSS 2017. Lecture Notes in Computer Science(), vol 10581. Springer, Cham. https://doi.org/10.1007/978-3-319-69471-9_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-69471-9_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69470-2
Online ISBN: 978-3-319-69471-9
eBook Packages: Computer ScienceComputer Science (R0)