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).
- Online social networks
- Link prediction
This is a preview of subscription content, access via your institution.
Tax calculation will be finalised at checkout
Purchases are for personal use onlyLearn about institutional subscriptions
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.
Bourdieu, P., & Wacquant, L. (1992). An invitation to reflexive sociology (1st ed.). Chicago: University of Chicago Press.
Kane, G., & Alavi, M. (2008). Casting the net: A multimodal network perspective on user-system interactions. Information Systems Research, 19(3), 253–272.
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.
Trier, M. (2008). Towards dynamic visualization for understanding evolution of digital communication networks. Information Systems Research, 19(3), 335–350.
Albert, R., & Barabási, A. (2000). Topology of evolving networks: Local events and universality. Physical Review Letters, 85(24), 5234–5237.
Newman, M. (2001). Clustering and preferential attachment in growing networks. Physical Review E, 64(2), 025102.
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.
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/
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
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.
Zheleva, E., & Getoor, L. (2007). Privacy in social networks: A survey (pp. 277–306). New York: Springer.
Yu, H. (2011). Sybil defences via social networks: A tutorial and survey. SIGACT News, 42(3), 80–101.
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.
Fire, M., Goldschmidt, R., & Elovici, Y. (2014). Online social networks: Threats and solutions. IEEE Communications Surveys and Tutorials, 16(4), 2019–2036.
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.
Cosley, D., Huttenlocher, D. P., Kleinberg, J. M., Lan, X., & Suri, S. (2010). Sequential influence models in social networks. ICWSM, 10, 26.
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.
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.
Kossinets, G., & Watts, D. J. (2006). Empirical analysis of an evolving social network. Science, 311(5757), 88–90.
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.
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.
Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology, 83(6), 1420–1443.
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.
Privacy: Stanford encyclopedia of philosophy, 2002.
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.
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.
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.
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.
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.
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).
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.
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.
Foster, T. N., & Greene, C. R. (2012). Legal issues of online social networks and the workplace. Journal of Law, Business and Ethics, 18, 131.
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.
Danezis, G., & Mittal, P. (2009, February). SybilInfer: Detecting sybil nodes using social networks. In NDSS (pp. 1–15).
Egele, M., Stringhini, G., Kruegel, C., & Vigna, G. (2013). Compa: Detecting compromised social network accounts. In Symposium on network and distributed system security (NDSS).
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.
Dittrich, D., Reiher, P., & Dietrich, S. (2004). Internet denial of service: Attack and defense mechanisms. London: Pearson Education.
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.
Cranor, L. F., Guduru, P., & Arjula, M. (2006). User interfaces for privacy agents. ACM Transactions on Computer-Human Interaction (TOCHI), 13(2), 135–178.
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.
Gao, H., Hu, J., Huang, T., Wang, J., & Chen, Y. (2011). Security issues in online social networks. IEEE Internet Computing, 15(4), 56–63.
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.
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.
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.
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.
Zheleva, E., & Getoor, L. (2011). Privacy in social networks: A survey. In Social network data analytics (pp. 277–306). Boston: Springer.
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.
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.
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.
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.
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).
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.
Korula, N., & Lattanzi, S. (2014). An efficient reconciliation algorithm for social networks. Proceedings of the VLDB Endowment, 7(5), 377–388.
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.
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.
Beigi, G., & Liu, H. (2018). Privacy in social media: Identification, mitigation and applications. arXiv preprint arXiv:1808.02191.
Zhang, Z., & Gupta, B. B. (2018). Social media security and trustworthiness: Overview and new direction. Future Generation Computer Systems, 86, 914–925.
Tucker, C. E. (2014). Social networks, personalized advertising, and privacy controls. Journal of Marketing Research, 51(5), 546–562.
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).
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.
Bringmann, K., Friedrich, T., & Krohmer, A. (2018). De-anonymization of heterogeneous random graphs in quasilinear time. Algorithmica, 80(11), 3397–3427.
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.
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.
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.
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.
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.
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.
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.
Adat, V., & Gupta, B. B. (2018). Security in Internet of Things: issues, challenges, taxonomy, and architecture. Telecommunication Systems, 67(3), 423–441.
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.
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.
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.
Gupta, B. B. (Ed.). (2018). Computer and cyber security: Principles, algorithm, applications, and perspectives. New York: CRC Press.
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.
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.
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.
Adamic, L. A., & Adar, E. (2003). Friends and neighbors on the web. Social Networks, 25(3), 211–230.
Watts, D., & Stogatz, S. (1998). Small world. Nature, 393, 440–442.
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.
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.
Katz, L. (1953). A new status index derived from sociometric analysis. Psychometrika, 18(1), 39–43.
Wang, C., Satuluri, V., & Parthasarathy, S. (2007, October). Local probabilistic models for link prediction. In ICDM (pp. 322–331). New York: IEEE.
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.
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).
Getoor, L., & Diehl, C. P. (2005). Link mining: A survey. Acm Sigkdd Explorations Newsletter, 7(2), 3–12.
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.
Veiga, M. H., & Eickhoff, C. (2016). Privacy leakage through innocent content sharing in online social networks. arXiv preprint arXiv:1607.02714.
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.
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.
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.
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.
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.
Li, T., Zhang, J., Philip, S. Y., Zhang, Y., & Yan, Y. (2018). Deep dynamic network embedding for link prediction. IEEE Access, 6, 29219–29230.
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.
Editors and Affiliations
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
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
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22276-5
Online ISBN: 978-3-030-22277-2