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World Wide Web

, Volume 22, Issue 6, pp 2321–2354 | Cite as

A check-in shielding scheme against acquaintance inference in location-based social networks

  • Bo-Heng Chen
  • Cheng-Te Li
  • Kun-Ta ChuangEmail author
Article
  • 120 Downloads

Abstract

Location-based social services such as Foursquare and Facebook Place allow users to perform check-ins at places and interact with each other in geography (e.g. check-in together). While existing studies have exhibited that the adversary can accurately infer social ties based on check-in data, the traditional check-in mechanism cannot protect the acquaintance privacy of users. In this work, therefore, we propose a novel shielding check-in system, whose goal is to guide users to check-in at secure places. We accordingly propose a novel research problem, Check-in Shielding against Acquaintance Inference (CSAI), which aims at recommending a list of secure places when users intend to check-ins so that the potential that the adversary correctly identifies the friends of users can be significantly reduced. We develop the Check-in Shielding Scheme (CSS) framework to solve the CSAI problem. CSS consists of two steps, namely estimating the social strength between users and generating a list of secure places. Experiments conducted on Foursquare and Gowalla check-in datasets show that CSS is able to not only outperform several competing methods under various scenario settings, but also lead to the check-in distance preserving and ensure the usability of the new check-in data in Point-of-Interest (POI) recommendation.

Keywords

Acquaintance inference Location-based social networks Check-in shielding 

Notes

Acknowledgments

This work was supported in part by Ministry of Science and Technology, R.O.C., under Contract 107-2221-E-006-165-MY2, 107-2218-E-006-040, 107-2321-B-006-017, 107-2636-E-006-002 (MOST Young Scholar Fellowship) and 107-2221-E-006-199. In addition, we also thank the support from Academia Sinica under the Grant AS-107-TP-A05. Also, we would like to thank anonymous reviewers and the editor for their very useful comments and suggestions.

References

  1. 1.
    Acs, G., Castelluccia, C.: A case study: privacy preserving release of spatio-temporal density in paris. In: Proceedings of ACM SIGKDD (2014)Google Scholar
  2. 2.
    Andrés, M.E., Bordenabe, N.E., Chatzikokolakis, K., Palamidessi, C.: Geo-indistinguishability: Differential privacy for location-based systems. In: Proceedings of ACM CCS (2013)Google Scholar
  3. 3.
    Backes, M., Humbert, M., Pang, J., Zhang, Y.: walk2friends: inferring social links from mobility profiles. In: Proceedings of ACM CCS (2017)Google Scholar
  4. 4.
    Bordenabe, N.E., Chatzikokolakis, K., Palamidessi, C.: Optimal geo-indistinguishable mechanisms for location privacy. In: Proceedings of ACM CCS (2014)Google Scholar
  5. 5.
    Cheng, R., Pang, J., Zhang, Y.: Inferring friendship from check-in data of location-based social networks. In: Proceedings of ASONAM (2015)Google Scholar
  6. 6.
    Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proceedings of ACM KDD (2011)Google Scholar
  7. 7.
    Cranshaw, J., Toch, E., Hong, J.I., Kittur, A., Sadeh, N.M.: Bridging the gap between physical location and online social networks. In: Proceedings of UbiComp (2010)Google Scholar
  8. 8.
    Dey, R., Jelveh, Z., Ross, K.W.: Facebook users have become much more private: a large-scale study. In: Proceedings of PerCom Workshops (2012)Google Scholar
  9. 9.
    Fire, M., Goldschmidt, R., Elovici, Y.: Online social networks: threats and solutions. IEEE Communications Surveys and Tutorials (2014)Google Scholar
  10. 10.
    Grover, A., Leskovec, J.: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016, pp. 855–864 (2016)Google Scholar
  11. 11.
    Hay, M., Miklau, G., Jensen, D.D., Towsley, D.F., Li, C.: Resisting structural re-identification in anonymized social networks. VLDB J (2010)Google Scholar
  12. 12.
    Hsieh, H.-P., Yan, R., Li, C.-T.: Where you go reveals who you know: Analyzing social ties from millions of footprints. In: Proceedings of ACM CIKM (2015)Google Scholar
  13. 13.
    Likhyani, A., Bedathur, S., Deepak, P.: Locate: influence quantification for location promotion in location-based social networks. In: Proceedings of IJCAI (2017)Google Scholar
  14. 14.
    Liu, K., Terzi, E.: Towards identity anonymization on graphs. In: Proceedings of ACM SIGMOD (2008)Google Scholar
  15. 15.
    Mir, D.J., Isaacman, S., Cáceres, R., Martonosi, M., Wright, R.N.: Dp-where: Differentially private modeling of human mobility. In: Proceedings of IEEE Big Data (2013)Google Scholar
  16. 16.
    Njoo, G.S., Kao, M.-C., Hsu, K.-W., Peng, W.-C.: Exploring check-in data to infer social ties in location based social networks. In: Proceedings of PAKDD (2017)Google Scholar
  17. 17.
    Noulas, A., Scellato, S., Lathia, N., Mascolo, C.: A random walk around the city New venue recommendation in location-based social networks. In: SocialCom/PASSAT, pp. 144–153 (2012)Google Scholar
  18. 18.
    Pham, H., Hu, L., Shahabi, C.: Towards integrating real-world spatiotemporal data with social networks. In: Proceedings of ACM SIGSPATIAL (2011)Google Scholar
  19. 19.
    Pham, H., Shahabi, C., Liu, Y.: EBM: an entropy-based model to infer social strength from spatiotemporal data. In: Proceedings of ACM SIGMOD (2013)Google Scholar
  20. 20.
    Pisinger, D.: Upper bounds and exact algorithms for p-dispersion problems. Computers & OR (2006)Google Scholar
  21. 21.
    Puttaswamy, K.P.N., Wang, S., Steinbauer, T., Agrawal, D., El Abbadi, A., Kruegel, C., Zhao, B.Y.: Preserving location privacy in geosocial applications. IEEE Transactions on Mobile Computing (2014)Google Scholar
  22. 22.
    Sun, C., Philip, S.Y., Kong, X., Fu, Y.: Privacy preserving social network publication against mutual friend attacks. In: Proceedings of ICDM Workshops (2013)Google Scholar
  23. 23.
    Tai, C.-H., Yu, P.S., Yang, D.-N., Chen, M.-S.: Privacy-preserving social network publication against friendship attacks. In: Proceedings of ACM SIGKDD (2011)Google Scholar
  24. 24.
    Wang, H., Li, Z., Lee, W.-C.: PGT: measuring mobility relationship using personal, global and temporal factors. In: Proceedings of IEEE ICDM (2014)Google Scholar
  25. 25.
    Wang, Y., Zheng, B.: Preserving privacy in social networks against connection fingerprint attacks. In: Proceedings of ICDE (2015)Google Scholar
  26. 26.
    Zhou, B., Pei, J., Luk, W.-S: A brief survey on anonymization techniques for privacy preserving publishing of social network data. SIGKDD Explorations (2008)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Graduate Program of Multimedia Systems and Intelligent ComputingNational Cheng Kung University and Academia SinicaTainanTaiwan
  2. 2.Department of Computer Science and Information EngineeringNational Cheng Kung UniversityTainanTaiwan
  3. 3.Department of StatisticsNational Cheng Kung UniversityTainanTaiwan
  4. 4.Institute of Data ScienceNational Cheng Kung UniversityTainanTaiwan

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