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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Privacy measurement method using a graph structure on online social networks

  • Li, XueFeng (School of Artificial Intelligence and Big Data, Henan University of Technology) ;
  • Zhao, Chensu (School of Cyberspace Security, Beijing University of Posts and Telecommunications) ;
  • Tian, Keke (School of Computer Science and Technology, Henan Polytechnic University)
  • Received : 2019.11.06
  • Accepted : 2020.12.21
  • Published : 2021.10.01
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Abstract

Recently, with an increase in Internet usage, users of online social networks (OSNs) have increased. Consequently, privacy leakage has become more serious. However, few studies have investigated the difference between privacy and actual behaviors. In particular, users' desire to change their privacy status is not supported by their privacy literacy. Presenting an accurate measurement of users' privacy status can cultivate the privacy literacy of users. However, the highly interactive nature of interpersonal communication on OSNs has promoted privacy to be viewed as a communal issue. As a large number of redundant users on social networks are unrelated to the user's privacy, existing algorithms are no longer applicable. To solve this problem, we propose a structural similarity measurement method suitable for the characteristics of social networks. The proposed method excludes redundant users and combines the attribute information to measure the privacy status of users. Using this approach, users can intuitively recognize their privacy status on OSNs. Experiments using real data show that our method can effectively and accurately help users improve their privacy disclosures.

Keywords

Acknowledgement

We are grateful to the students, teachers, and staff who helped us collect data, as well as the social network users who were willing to contribute their personal information and the school for their support in our research work.

References

  1. Z. He, Z. Cai, and J. Yu, Latent-data privacy preserving with customized data utility for social network data, IEEE Trans. Veh. Technol. 67 (2018), 665-673. https://doi.org/10.1109/tvt.2017.2738018
  2. Z. He, Z. Cai, and X. Wang, Modeling propagation dynamics and developing optimized countermeasures for rumor spreading in online social networks, in Proc. IEEE Int. Conf. Distrib. Comput. Syst. (ICDCS) (Columbus, OH, USA), July 2015, pp. 205-214.
  3. M. Bartsch and T. Dienlin, Control your facebook: An analysis of online privacy literacy, Comput. Hum. Behav. 56 (2016), 147-154. https://doi.org/10.1016/j.chb.2015.11.022
  4. J. Ge, J. Peng, and Z. Chen, Your privacy information are leaking when you surfing on the social networks: A survey of the degree of online self-disclosure (DOSD), in Proc. IEEE Int. Conf. Cogn. Inf. Cogn. Comput. (ICCI* CC) (London, UK), Aug. 2014, pp. 329-336.
  5. J. Jiang, Personal information leakage on the rise in China: Report, 2017, available at http://en.people.cn/n3/2017/0331/c9000 0-9197748.html [last accessed 31 March 2017].
  6. C. Joshi and U. K. Singh, Information security risks management framework- A step towards mitigating security risks in university network, J. Inf. Sec. Appl. 35 (2017), 128-137. https://doi.org/10.1016/j.jisa.2017.06.006
  7. T. Dienlin and S. Trepte, Is the privacy paradox a relic of the past? an in-depth analysis of privacy attitudes and privacy behaviors, Eur. J. Soc. Psychol. 45 (2015), 285-297. https://doi.org/10.1002/ejsp.2049
  8. K. M. Altenburger and J Ugander, Monophily in social networks introduces similarity among friends-of-friends, Nat. Hum. Behav. 2 (2018), no. 4, 284. https://doi.org/10.1038/s41562-018-0321-8
  9. A. De Salve et al., Discovering homophily in online social networks, Mob. Netw. Appl. 23 (2018), 1715-1726. https://doi.org/10.1007/s11036-018-1067-2
  10. Q. Fang et al., Relational user attribute inference in social media, IEEE Trans. Multimedia 17 (2015), 1031-1044. https://doi.org/10.1109/TMM.2015.2430819
  11. R. K. Wong and B. S. Vidyalakshmi, Privacy leakage via attribute inference in directed social networks, in Information and Communications Security, Springer, Cham, Switzerland, 2016, pp. 333-346.
  12. F. Al Zamal, W. Liu, and D. Ruths, Homophily and latent attribute inference: Inferring latent attributes of twitter users from neighbors, in Proc. Int. AAAI Conf. Weblogs Soc. Media (Dublin, Ireland), June 2012.
  13. N. Z. Gong and B. Liu, You are who you know and how you behave: Attribute inference attacks via users' social friends and behaviors, in Proc. USENIX Sec. Symp. (Austin, TX, USA), Aug. 2016, pp. 979- 995.
  14. K. Thomas, C. Grier, and D. M. Nicol, Unfriendly: Multi-party privacy risks in social networks, in Privacy Enhancing Technologies, vol. 6205, Springer, Berlin, Heidelberg, 2010, pp. 236-252.
  15. N. Z. Gong et al., Joint link prediction and attribute inference using a social-attribute network, ACM Trans. Intell. Syst. Technol. (TIST) 5 (2014), 27.
  16. N. Z. Gong and B. Liu, Attribute inference attacks in online social networks, ACM Trans. Priv. Sec. 21 (2018), 1-30, article no. 3.
  17. M. B. Islam et al., A greater understanding of social networks privacy requirements: The user perspective, J. Inf. Sec. Appl. 33 (2017), 30-44. https://doi.org/10.1016/j.jisa.2017.01.004
  18. I. F. Lam, K. T. Chen, and L. J. Chen, Involuntary information leakage in social network services, in International Workshop on Security, vol. 5312, Springer, Berlin, Germany, 2008, pp. 167 -183.
  19. C. Patsakis et al., Distributing privacy policies over multimedia content across multiple online social networks, Comput. Netw. 75 (2014), 531-543. https://doi.org/10.1016/j.comnet.2014.08.023
  20. R. Dey et al., Estimating age privacy leakage in online social networks, in Proc. INFOCOM (Orlando, FL, USA), Mar. 2012, pp. 2836-2840.
  21. K. Liang et al., Privacy concerns for photo sharing in online social networks, IEEE Internet Comput. 19 (2015), 58-63.
  22. A. Srivastava and G. Geethakumari, Measuring privacy leaks in online social networks, in Proc. Int. Conf. Adv. Comput, Comm. Inf. (ICACCI), (Mysore, India), Aug. 2013, pp. 2095-2100.
  23. E. M. Maximilien et al., Privacy-as-a-service: Models, algorithms, and results on the Facebook platform, in Proc. Web 2.0 Sec. Priv. Workshop, vol. 2, 2009.
  24. K. Liu and E. Terzi, A framework for computing the privacy scores of users in online social networks, ACM Trans. Knowl. Discov. Data 5 (2010), 6.
  25. L. Fang and K. LeFevre, Privacy wizards for social networking sites, in Proc. Int. Conf. World Wide Web (Raleigh, NC, USA), Apr. 2010, pp. 351-360.
  26. K. Li, X. Lin, and X. Wang, An empirical analysis of users' privacy disclosure behaviors on social network sites, Inf. Manag. 52 (2015), 882-891. https://doi.org/10.1016/j.im.2015.07.006
  27. S. Jain and S. K. Raghuwanshi, Fine grained privacy measuring of user's profile over online social network, in Intelligent Communication and Computational Technologies, vol. 19, Springer, Singapore, Singapore, pp. 371-379.
  28. Y. Zeng et al., Trust-aware privacy evaluation in online social networks, in Proc. IEEE Int. Conf. Commun. (ICC) (Sydney, Australia), June 2014, pp. 932-938.
  29. M. Li, Z. Liu, and K. Dong, Privacy preservation in social network against public neighborhood attacks, in Proc. Trustcom/BigDataSE/ISPA (Tianjin, China), Aug. 2016, pp. 1575-1580.
  30. J. L. Becker and H. Chen, Measuring privacy risk in online social networks, Int. J. Secur., Priv. Trust Manag. 4 (2009), 2095-2100.
  31. Y. Wang, R. K. Nepali, and J. Nikolai, Social network privacy measurement and simulation, in Proc. Int. Conf. Comput., Netw. Commun. (ICNC) (Honolulu, HI, USA), Feb. (2014), 802-806.
  32. Y. Alsarkal, N. Zhang, and H. Xu, Your privacy is your friend's privacy: examining interdependent information disclosure on online social networks, in Proc. Hawaii Int. Conf. Syst. Sci. (Waikoloa, HI, USA), Jan. 2018.
  33. R. G. Pensa and G. Di Blasi, A privacy self-assessment framework for online social networks, Expert Syst. Appl. 86 (2017), 18-31. https://doi.org/10.1016/j.eswa.2017.05.054
  34. E. Aghasian et al., Scoring users' privacy disclosure across multiple online social networks, IEEE Access 5 (2017), 13118-13130. https://doi.org/10.1109/ACCESS.2017.2720187
  35. K. Renaud and S. Flowerday, Contemplating human-centred security & privacy research: Suggesting future directions, J. Inf. Sec. Appl. 34 (2017), 76-81. https://doi.org/10.1016/j.jisa.2017.05.006
  36. W Wang et al., Coevolution spreading in complex networks, Phy. Rev. 820 (2019), 1-51.
  37. W. Wang, H. E. Stanley, and L. A. Braunstein, Effects of time-delays in the dynamics of social contagions, New J. Phy. 20 (2017), 013034. https://doi.org/10.1088/1367-2630/20/1/013034
  38. W. Wang et al., Containing misinformation spreading in temporal social networks, Chaos 29 (2019), 123131. https://doi.org/10.1063/1.5114853
  39. G. Jeh and J. Widom, SimRank: A measure of structural-context similarity, in Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. (Edmonton, Canada), July 2019, pp. 538-543.
  40. K. Lei et al., Understanding user behavior in sina weibo online social network: A Community Approach, IEEE Access 6 (2018), 13302-13316. https://doi.org/10.1109/access.2018.2808158
  41. X. Li et al., A privacy measurement framework for multiple online social networks against social identity linkage, Appl. Sci. 8 (2018), 1790. https://doi.org/10.3390/app8101790