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Security and Privacy in Social Networks: Data and Structural Anonymity

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Abstract

Social networking has become an inevitable catchline among teenagers as well as today’s older generation. In recent years, there has been observed remarkable growth in social networking sites, especially in terms of adaptability as well as popularity both in the media and academia. The information present on social networking sites is used in social, geographic and economic analysis, thereby giving meaningful insights. Although publishing of such analysis may create serious security threats, users sharing personal information on these social platforms may face privacy breach. Various third-party applications are making use of network data for advertisement, academic research and application development which can also raise security and privacy concerns. This chapter has a binary focus towards studying and analysing security and privacy threats prevailing and providing a detailed description regarding solutions that will aid towards sustaining user’s privacy and security. Currently, there exist multiple privacy techniques that propose solutions for maintaining user anonymity on online social networks. The chapter also highlights all the available techniques as well as the issue and challenges surrounding their real-world implementation. The goal of such mechanisms is to push deterged data on social platforms, thereby strengthening user privacy despite of the sensitive information shared on online social networks (OSN). While such mechanisms have gathered researcher’s attention for their simplicity, their ability to preserve the user’s privacy still struggles with regard to preserving useful knowledge contained in it. Thus, anonymization of OSN might lead to certain information loss. This chapter explores multiple data and structural anonymity techniques for modelling, evaluating and managing user’s privacy risks cum concerns with respect to online social networks (OSNs).

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References

  1. Hsu, C., Wang, C., & Tai, Y. (2011). The closer the relationship, the more the interaction on Facebook? Investigating the case of Taiwan users. Cyberpsychology, Behavior and Social Networking, 14(7–8), 473–476.

    Article  Google Scholar 

  2. Bourdieu, P., & Wacquant, L. (1992). An invitation to reflexive sociology (1st ed.). Chicago: University of Chicago Press.

    Google Scholar 

  3. Kane, G., & Alavi, M. (2008). Casting the net: A multimodal network perspective on user-system interactions. Information Systems Research, 19(3), 253–272.

    Article  Google Scholar 

  4. Ellison, N., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook “Friends:” Social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 1143–1168.

    Article  Google Scholar 

  5. Trier, M. (2008). Towards dynamic visualization for understanding evolution of digital communication networks. Information Systems Research, 19(3), 335–350.

    Article  Google Scholar 

  6. Albert, R., & Barabási, A. (2000). Topology of evolving networks: Local events and universality. Physical Review Letters, 85(24), 5234–5237.

    Article  Google Scholar 

  7. Newman, M. (2001). Clustering and preferential attachment in growing networks. Physical Review E, 64(2), 025102.

    Article  Google Scholar 

  8. Carlyne, L., & Kujath, B. (2011). Facebook and MySpace: Complement or substitute for face-to-face interaction? Cyberpsychology, Behavior and Social Networking, 14(1–2), 75–78.

    Google Scholar 

  9. Dam, W. B. (2009). School teacher suspended for Facebook gun photo. http://www.foxnews.com/story/2009/02/05/schoolteacher-suspended-for-facebook-gun-photo/

  10. Mail, D. (2011). Bank worker fired for Facebook post comparing her 7-an-hour wage to Lloyds boss’s 4000-an-hour salary. http://dailym.ai/fjRTlC

  11. Narayanan, A., Shi, E., & Rubinstein, B. I. (2011). Link prediction by de-anonymization: How we won the Kaggle social network challenge. In Proceedings of the 2011 international joint conference on neural networks (IJCNN) (pp. 1825–1834). New York: IEEE.

    Chapter  Google Scholar 

  12. Zheleva, E., & Getoor, L. (2007). Privacy in social networks: A survey (pp. 277–306). New York: Springer.

    Google Scholar 

  13. Yu, H. (2011). Sybil defences via social networks: A tutorial and survey. SIGACT News, 42(3), 80–101.

    Article  Google Scholar 

  14. Zhang, C., Sun, J., Zhu, X., & Fang, Y. (2010). Privacy and security for online social networks: Challenges and opportunities. IEEE Network, 24(4), 13–18.

    Article  Google Scholar 

  15. Fire, M., Goldschmidt, R., & Elovici, Y. (2014). Online social networks: Threats and solutions. IEEE Communications Surveys and Tutorials, 16(4), 2019–2036.

    Article  Google Scholar 

  16. Baagyere, E. Y., Qin, Z., Xiong, H., & Zhiguang, Q. (2016). The structural properties of online social networks and their application areas. IAENG International Journal of Computer Science, 43(2), 2.

    Google Scholar 

  17. Cosley, D., Huttenlocher, D. P., Kleinberg, J. M., Lan, X., & Suri, S. (2010). Sequential influence models in social networks. ICWSM, 10, 26.

    Google Scholar 

  18. Goyal, A., Bonchi, F., & Lakshmanan, L. V. (2010, February). Learning influence probabilities in social networks. In Proceedings of the third ACM international conference on web search and data mining (pp. 241–250). New York: ACM.

    Chapter  Google Scholar 

  19. Backstrom, L., Huttenlocher, D., Kleinberg, J., & Lan, X. (2006, August). Group formation in large social networks: Membership, growth, and evolution. In Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 44–54). New York: ACM.

    Chapter  Google Scholar 

  20. Kossinets, G., & Watts, D. J. (2006). Empirical analysis of an evolving social network. Science, 311(5757), 88–90.

    Article  MathSciNet  MATH  Google Scholar 

  21. Richardson, M., & Domingos, P. (2002, July). Mining knowledge-sharing sites for viral marketing. In Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 61–70). New York: ACM.

    Chapter  Google Scholar 

  22. Domingos, P., & Richardson, M. (2001, August). Mining the network value of customers. In Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining (pp. 57–66). New York: ACM.

    Chapter  Google Scholar 

  23. Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology, 83(6), 1420–1443.

    Article  Google Scholar 

  24. Kempe, D., Kleinberg, J., & Tardos, É. (2003, August). Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 137–146). New York: ACM.

    Chapter  Google Scholar 

  25. Privacy: Stanford encyclopedia of philosophy, 2002.

    Google Scholar 

  26. Spiekermann, S., Grossklags, J., & Berendt, B. (2001, October). E-privacy in 2nd generation E-commerce: Privacy preferences versus actual behavior. In Proceedings of the 3rd ACM conference on electronic commerce (pp. 38–47). New York: ACM.

    Google Scholar 

  27. Boshmaf, Y., Muslukhov, I., Beznosov, K., & Ripeanu, M. (2011, December). The socialbot network: When bots socialize for fame and money. In Proceedings of the 27th annual computer security applications conference (pp. 93–102). New York: ACM.

    Google Scholar 

  28. Bilge, L., Strufe, T., Balzarotti, D., & Kirda, E. (2009, April). All your contacts are belong to us: Automated identity theft attacks on social networks. In Proceedings of the 18th international conference on World wide web (pp. 551–560). New York: ACM.

    Google Scholar 

  29. Mahmood, S. (2012, November). New privacy threats for Facebook and twitter users. In 2012 Seventh international conference on P2P, parallel, grid, cloud and internet computing (3PGCIC) (pp. 164–169). New York: IEEE.

    Chapter  Google Scholar 

  30. Dey, R., Tang, C., Ross, K., & Saxena, N. (2012, March). Estimating age privacy leakage in online social networks. In INFOCOM, 2012 proceedings IEEE (pp. 2836–2840). New York: IEEE.

    Chapter  Google Scholar 

  31. Chaabane, A., Acs, G., & Kaafar, M. A. (2012, February). You are what you like! Information leakage through users’ interests. In Proceedings of the 19th Annual Network & Distributed System Security Symposium (NDSS).

    Google Scholar 

  32. Power, R., & Forte, D. (2008). War & peace in cyberspace: Don’t Twitter away your organisation’s secrets. Computer Fraud and Security, 2008(8), 18–20.

    Article  Google Scholar 

  33. Wen, S., Haghighi, M. S., Chen, C., Xiang, Y., Zhou, W., & Jia, W. (2015). A sword with two edges: Propagation studies on both positive and negative information in online social networks. IEEE Transactions on Computers, 64(3), 640–653.

    Article  MathSciNet  MATH  Google Scholar 

  34. Foster, T. N., & Greene, C. R. (2012). Legal issues of online social networks and the workplace. Journal of Law, Business and Ethics, 18, 131.

    Google Scholar 

  35. Viswanath, B., Post, A., Gummadi, K. P., & Mislove, A. (2011). An analysis of social network-based sybil defenses. ACM SIGCOMM Computer Communication Review, 41(4), 363–374.

    Article  Google Scholar 

  36. Danezis, G., & Mittal, P. (2009, February). SybilInfer: Detecting sybil nodes using social networks. In NDSS (pp. 1–15).

    Google Scholar 

  37. Egele, M., Stringhini, G., Kruegel, C., & Vigna, G. (2013). Compa: Detecting compromised social network accounts. In Symposium on network and distributed system security (NDSS).

    Google Scholar 

  38. Heymann, P., Koutrika, G., & Garcia-Molina, H. (2007). Fighting spam on social web sites: A survey of approaches and future challenges. IEEE Internet Computing, 11(6), 36–45.

    Article  Google Scholar 

  39. Dittrich, D., Reiher, P., & Dietrich, S. (2004). Internet denial of service: Attack and defense mechanisms. London: Pearson Education.

    Google Scholar 

  40. Huber, M., Mulazzani, M., Weippl, E., Kitzler, G., & Goluch, S. (2011). Friend-in-the-middle attacks: Exploiting social networking sites for spam. IEEE Internet Computing, 15(3), 28–34.

    Article  Google Scholar 

  41. Cranor, L. F., Guduru, P., & Arjula, M. (2006). User interfaces for privacy agents. ACM Transactions on Computer-Human Interaction (TOCHI), 13(2), 135–178.

    Article  Google Scholar 

  42. Danezis, G., Domingo-Ferrer, J., Hansen, M., Hoepman, J. H., Metayer, D. L., Tirtea, R., & Schiffner, S. (2015). Privacy and data protection by design-from policy to engineering. arXiv preprint arXiv:1501.03726.

    Google Scholar 

  43. Gao, H., Hu, J., Huang, T., Wang, J., & Chen, Y. (2011). Security issues in online social networks. IEEE Internet Computing, 15(4), 56–63.

    Article  Google Scholar 

  44. Guha, S., Tang, K., & Francis, P. (2008, August). NOYB: Privacy in online social networks. In Proceedings of the first workshop on online social networks (pp. 49–54). New York: ACM.

    Chapter  Google Scholar 

  45. Debatin, B., Lovejoy, J. P., Horn, A. K., & Hughes, B. N. (2009). Facebook and online privacy: Attitudes, behaviors, and unintended consequences. Journal of Computer-Mediated Communication, 15(1), 83–108.

    Article  Google Scholar 

  46. Zhou, B., & Pei, J. (2008, April). Preserving privacy in social networks against neighborhood attacks. In IEEE 24th international conference on data engineering, 2008. ICDE 2008 (pp. 506–515). New York: IEEE.

    Google Scholar 

  47. Heatherly, R., Kantarcioglu, M., & Thuraisingham, B. (2013). Preventing private information inference attacks on social networks. IEEE Transactions on Knowledge and Data Engineering, 25(8), 1849–1862.

    Article  Google Scholar 

  48. Zheleva, E., & Getoor, L. (2011). Privacy in social networks: A survey. In Social network data analytics (pp. 277–306). Boston: Springer.

    Chapter  Google Scholar 

  49. Tripathy, B. K., Sishodia, M. S., Jain, S., & Mitra, A. (2014). Privacy and anonymization in social networks. In Social networking (pp. 243–270). Cham: Springer.

    Chapter  Google Scholar 

  50. Machanavajjhala, A., Gehrke, J., Kifer, D., & Venkitasubramaniam, M. (2006, April). \ell-Diversity: Privacy beyond\kappa-anonymity. In 22nd International Conference on Data Engineering (ICDE’06) (p. 24). New York: IEEE.

    Chapter  Google Scholar 

  51. Li, N., Li, T., & Venkatasubramanian, S. (2007, April). T-closeness: Privacy beyond k-anonymity and l-diversity. In IEEE 23rd international conference on data engineering, 2007. ICDE 2007 (pp. 106–115). New York: IEEE.

    Chapter  Google Scholar 

  52. Backstrom, L., Dwork, C., & Kleinberg, J. (2007, May). Wherefore art thou r3579x?: Anonymized social networks, hidden patterns, and structural steganography. In Proceedings of the 16th international conference on world wide web (pp. 181–190). New York: ACM.

    Chapter  Google Scholar 

  53. Zhang, A., Xie, X., Chang, K. C. C., Gunter, C. A., Han, J., & Wang, X. (2014, March). Privacy risk in anonymized heterogeneous information networks. In EDBT (pp. 595–606).

    Google Scholar 

  54. Yartseva, L., & Grossglauser, M. (2013, October). On the performance of percolation graph matching. In Proceedings of the first ACM conference on online social networks (pp. 119–130). New York: ACM.

    Chapter  Google Scholar 

  55. Korula, N., & Lattanzi, S. (2014). An efficient reconciliation algorithm for social networks. Proceedings of the VLDB Endowment, 7(5), 377–388.

    Article  Google Scholar 

  56. Ji, S., Li, W., Gong, N. Z., Mittal, P., & Beyah, R. A. (2015, February). On your social network de-anonymizablity: Quantification and large scale evaluation with seed knowledge. In NDSS.

    Google Scholar 

  57. Ji, S., Li, W., Gong, N. Z., Mittal, P., & Beyah, R. A. (2016). Seed based deanonymizability quantification of social networks. IEEE Transactions on Information Forensics and Security (TIFS), 11(7), 1398–1411.

    Article  Google Scholar 

  58. Beigi, G., & Liu, H. (2018). Privacy in social media: Identification, mitigation and applications. arXiv preprint arXiv:1808.02191.

    Google Scholar 

  59. Zhang, Z., & Gupta, B. B. (2018). Social media security and trustworthiness: Overview and new direction. Future Generation Computer Systems, 86, 914–925.

    Article  Google Scholar 

  60. Tucker, C. E. (2014). Social networks, personalized advertising, and privacy controls. Journal of Marketing Research, 51(5), 546–562.

    Article  Google Scholar 

  61. Neal, Z., Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social networks (p. 296). Thousand Oaks: Sage. 54.00(paper), 130.00 (cloth).

    Google Scholar 

  62. Chiasserini, C. F., Garetto, M., & Leonardi, E. (2018). De-anonymizing clustered social networks by percolation graph matching. ACM Transactions on Knowledge Discovery from Data (TKDD), 12(2), 21.

    Article  Google Scholar 

  63. Bringmann, K., Friedrich, T., & Krohmer, A. (2018). De-anonymization of heterogeneous random graphs in quasilinear time. Algorithmica, 80(11), 3397–3427.

    Article  MathSciNet  MATH  Google Scholar 

  64. Fu, H., Zhang, A., & Xie, X. (2014, April). De-anonymizing social graphs via node similarity. In Proceedings of the 23rd international conference on world wide web (pp. 263–264). New York: ACM.

    Google Scholar 

  65. Fu, H., Zhang, A., & Xie, X. (2015). Effective social graph deanonymization based on graph structure and descriptive information. ACM Transactions on Intelligent Systems and Technology (TIST), 6(4), 49.

    Google Scholar 

  66. Sharad, K., & Danezis, G. (2014, November). An automated social graph de-anonymization technique. In Proceedings of the 13th workshop on privacy in the electronic society (pp. 47–58). New York: ACM.

    Google Scholar 

  67. Aghasian, E., Garg, S., Gao, L., Yu, S., & Montgomery, J. (2017). Scoring users’ privacy disclosure across multiple online social networks. IEEE Access, 5, 13118–13130.

    Article  Google Scholar 

  68. Liu, K., & Terzi, E. (2010). A framework for computing the privacy scores of users in online social networks. ACM Transactions on Knowledge Discovery from Data (TKDD), 5(1), 6.

    Article  Google Scholar 

  69. Domingo-Ferrer, J. (2010, October). Rational privacy disclosure in social networks. In International conference on modeling decisions for artificial intelligence (pp. 255–265). Berlin/Heidelberg: Springer.

    Chapter  Google Scholar 

  70. Tewari, A., & Gupta, B. B. (2018). Security, privacy and trust of different layers in Internet-of-Things (IoTs) framework. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2018.04.027.

  71. Adat, V., & Gupta, B. B. (2018). Security in Internet of Things: issues, challenges, taxonomy, and architecture. Telecommunication Systems, 67(3), 423–441.

    Article  Google Scholar 

  72. Gupta, B. B., Gupta, S., & Chaudhary, P. (2017). Enhancing the browser-side context-aware sanitization of suspicious HTML5 code for halting the DOM-based XSS vulnerabilities in cloud. International Journal of Cloud Applications and Computing (IJCAC), 7(1), 1–31.

    Article  Google Scholar 

  73. Stutzman, F., & Kramer-Duffield, J. (2010, April). Friends only: Examining a privacy-enhancing behavior in Facebook. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1553–1562). New York: ACM.

    Google Scholar 

  74. Pempek, T. A., Yermolayeva, Y. A., & Calvert, S. L. (2009). College students’ social networking experiences on Facebook. Journal of Applied Developmental Psychology, 30(3), 227–238.

    Article  Google Scholar 

  75. Gupta, B. B. (Ed.). (2018). Computer and cyber security: Principles, algorithm, applications, and perspectives. New York: CRC Press.

    Google Scholar 

  76. Wang, P., Xu, B., Wu, Y., & Zhou, X. (2015). Link prediction in social networks: The state-of-the-art. Science China Information Sciences, 58(1), 1–38.

    Google Scholar 

  77. Tang, J., Chang, S., Aggarwal, C., & Liu, H. (2015, February). Negative link prediction in social media. In Proceedings of the eighth ACM international conference on web search and data mining (pp. 87–96). New York: ACM.

    Google Scholar 

  78. Daminelli, S., Thomas, J. M., Durán, C., & Cannistraci, C. V. (2015). Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks. New Journal of Physics, 17(11), 113037.

    Article  Google Scholar 

  79. Adamic, L. A., & Adar, E. (2003). Friends and neighbors on the web. Social Networks, 25(3), 211–230.

    Article  Google Scholar 

  80. Watts, D., & Stogatz, S. (1998). Small world. Nature, 393, 440–442.

    Article  Google Scholar 

  81. Al Hasan, M., Chaoji, V., Salem, S., & Zaki, M. (2006, April). Link prediction using supervised learning. In SDM06: Workshop on link analysis, counter-terrorism and security.

    Google Scholar 

  82. Liben-Nowell, D., & Kleinberg, J. (2007). The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58(7), 1019–1031.

    Article  Google Scholar 

  83. Katz, L. (1953). A new status index derived from sociometric analysis. Psychometrika, 18(1), 39–43.

    Article  MATH  Google Scholar 

  84. Wang, C., Satuluri, V., & Parthasarathy, S. (2007, October). Local probabilistic models for link prediction. In ICDM (pp. 322–331). New York: IEEE.

    Google Scholar 

  85. Kashima, H., & Abe, N. (2006, December). A parameterized probabilistic model of network evolution for supervised link prediction. In Sixth international conference on data mining, 2006. ICDM’06 (pp. 340–349). New York: IEEE.

    Google Scholar 

  86. Taskar, B., Wong, M. F., Abbeel, P., & Koller, D. (2004). Link prediction in relational data. In Advances in neural information processing systems (pp. 659–666).

    Google Scholar 

  87. Getoor, L., & Diehl, C. P. (2005). Link mining: A survey. Acm Sigkdd Explorations Newsletter, 7(2), 3–12.

    Article  Google Scholar 

  88. Abawajy, J. H., Ninggal, M. I. H., & Herawan, T. (2016). Privacy preserving social network data publication. IEEE Communications Surveys and Tutorials, 18(3), 1974–1997.

    Article  Google Scholar 

  89. Veiga, M. H., & Eickhoff, C. (2016). Privacy leakage through innocent content sharing in online social networks. arXiv preprint arXiv:1607.02714.

    Google Scholar 

  90. Gupta, S., & Gupta, B. B. (2017). Detection, avoidance, and attack pattern mechanisms in modern web application vulnerabilities: Present and future challenges. International Journal of Cloud Applications and Computing (IJCAC), 7(3), 1–43.

    Article  Google Scholar 

  91. Stergiou, C., Psannis, K. E., Kim, B. G., & Gupta, B. (2018). Secure integration of IoT and cloud computing. Future Generation Computer Systems, 78, 964–975.

    Article  Google Scholar 

  92. Correa, T., Hinsley, A. W., & De Zuniga, H. G. (2010). Who interacts on the web?: The intersection of users’ personality and social media use. Computers in Human Behavior, 26(2), 247–253.

    Article  Google Scholar 

  93. Jyothi, V., & Kumari, V. V. (2016, August). Privacy preserving in dynamic social networks. In Proceedings of the international conference on informatics and analytics (p. 79). New York: ACM.

    Google Scholar 

  94. Ahmed, N. M., Chen, L., Wang, Y., Li, B., Li, Y., & Liu, W. (2018). DeepEye: Link prediction in dynamic networks based on non-negative matrix factorization. Big Data Mining and Analytics, 1(1), 19–33.

    Article  Google Scholar 

  95. Li, T., Zhang, J., Philip, S. Y., Zhang, Y., & Yan, Y. (2018). Deep dynamic network embedding for link prediction. IEEE Access, 6, 29219–29230.

    Article  Google Scholar 

  96. Narayanan, A., & Shmatikov, V. (2009, May). De-anonymizing social networks. In 30th IEEE symposium on security and privacy, 2009 (pp. 173–187). New York: IEEE.

    Chapter  Google Scholar 

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Jain, R., Jain, N., Nayyar, A. (2020). Security and Privacy in Social Networks: Data and Structural Anonymity. In: Gupta, B., Perez, G., Agrawal, D., Gupta, D. (eds) Handbook of Computer Networks and Cyber Security. Springer, Cham. https://doi.org/10.1007/978-3-030-22277-2_11

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