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COIP—Continuous, Operable, Impartial, and Privacy-Aware Identity Validity Estimation for OSN Profiles

Published:13 December 2016Publication History
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Abstract

Identity validation of Online Social Networks’ (OSNs’) peers is a critical concern to the insurance of safe and secure online socializing environments. Starting from the vision of empowering users to determine the validity of OSN identities, we suggest a framework to estimate the trustworthiness of online social profiles based only on the information they contain. Our framework is based on learning identity correlations between profile attributes in an OSN community and on collecting ratings from OSN community members to evaluate the trustworthiness of target profiles. Our system guarantees utility, user anonymity, impartiality in rating, and operability within the dynamics and continuous evolution of OSNs. In this article, we detail the system design, and we prove its correctness against these claimed quality properties. Moreover, we test its effectiveness, feasibility, and efficiency through experimentation on real-world datasets from Facebook and Google+, in addition to using the Adults UCI dataset.

References

  1. Cuneyt Gurcan Akcora, Barbara Carminati, and Elena Ferrari. 2011. Network and profile based measures for user similarities on social networks. In Proceedings of the 2011 IEEE International Conference on Information Reuse and Integration (IRI). IEEE, 292--298.Google ScholarGoogle ScholarCross RefCross Ref
  2. Lorenzo Alvisi, Allen Clement, Alessandro Epasto, Silvio Lattanzi, and Alessandro Panconesi. 2013. Sok: The evolution of Sybil defense via social networks. In Proceedings of the 2013 IEEE Symposium on Security and Privacy (SP). IEEE, 382--396. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. John C. Baez, Tobias Fritz, and Tom Leinster. 2011. A characterization of entropy in terms of information loss. Entropy 13, 11 (2011), 1945--1957.Google ScholarGoogle ScholarCross RefCross Ref
  4. Leila Bahri, Barbara Carminati, and Elena Ferrari. 2014. Community-based identity validation in online social networks. In Proceedings of the 34th International Conference on Distributed Computing Systems. IEEE. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Derek E. Bambauer. 2013. Privacy versus security. J. Criminal Law Criminol. 103.3: 667--683 (2013).Google ScholarGoogle Scholar
  6. Valerie Ria Boquiron. 2011. Spam, Scams and Other Social Media Threats. Retrieved Augsut 20, 2015 from http://www.trendmicro.com/vinfo/us/threat-encyclopedia/web-attack/75/spam-scams-and-other-social-media-threats. (2011).Google ScholarGoogle Scholar
  7. Yazan Boshmaf, Ildar Muslukhov, Konstantin Beznosov, and Matei Ripeanu. 2011. The socialbot network: When bots socialize for fame and money. In Proceedings of the 27th Annual Computer Security Applications Conference. ACM, 93--102. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Justin Brickell and Vitaly Shmatikov. 2008. The cost of privacy: Destruction of data-mining utility in anonymized data publishing. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 70--78. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Xiongcai Cai, Michael Bain, Alfred Krzywicki, Wayne Wobcke, Yang Sok Kim, Paul Compton, and Ashesh Mahidadia. 2011. Collaborative filtering for people to people recommendation in social networks. In AI 2010: Advances in Artificial Intelligence. Springer, 476--485.Google ScholarGoogle Scholar
  10. Jianneng Cao, Barbara Carminati, Elena Ferrari, and Kian Lee Tan. 2008. CASTLE: A delay-constrained scheme for k s-anonymizing data streams. In Proceedings of the IEEE 24th International Conference on Data Engineering, 2008 (ICDE 2008).IEEE, 1376--1378. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Jianneng Cao and Panagiotis Karras. 2012. Publishing microdata with a robust privacy guarantee. Proc. VLDB Endow. 5, 11 (2012), 1388--1399. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Qiang Cao, Michael Sirivianos, Xiaowei Yang, and Tiago Pregueiro. 2012. Aiding the detection of fake accounts in large scale social online services. In Presented as part of the 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12). 197--210. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Qiang Cao, Xiaowei Yang, Jieqi Yu, and Christopher Palow. 2014. Uncovering large groups of active malicious accounts in online social networks. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security. ACM, 477--488. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Tom Carter and Santa Fe. 2007. An introduction to information theory and entropy. Complex Systems Summer School, Santa Fe (2007).Google ScholarGoogle Scholar
  15. Chris Clifton and Tamir Tassa. 2013. On syntactic anonymity and differential privacy. In ICDE Workshops. 88--93.Google ScholarGoogle ScholarCross RefCross Ref
  16. Thomas M. Cover and Joy A. Thomas. 2012. Elements of Information Theory. John Wiley 8 Sons, New York, NY.Google ScholarGoogle Scholar
  17. George Danezis and Prateek Mittal. 2009. SybilInfer: Detecting Sybil nodes using social networks. In NDSS. San Diego, CA.Google ScholarGoogle Scholar
  18. Anhai Doan, Raghu Ramakrishnan, and Alon Y. Halevy. 2011. Crowdsourcing systems on the world-wide web. Commun. ACM 54, 4 (2011), 86--96. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Joseph S. Dumas. 2002. User-based evaluations. In The Human-Computer Interaction Handbook. L. Erlbaum Associates Inc., 1093--1117. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Cynthia Dwork. 2008. Differential privacy: A survey of results. In Theory and Applications of Models of Computation. Springer, 1--19. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Cynthia Dwork, Moni Naor, Toniann Pitassi, and Guy N. Rothblum. 2010. Differential privacy under continual observation. In Proceedings of the 42nd ACM Symposium on Theory of Computing. ACM, 715--724. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Lutz Finger. 2015. How To Spot Social Media Bots—They Are Often Lonely. (2015). Retrieved September 15, 2015 from http://www.forbes.com/sites/lutzfinger/2015/02/24/how-to-spot-social-media-bots-they-are-often-lonely/.Google ScholarGoogle Scholar
  23. Ben Foster. 2014. How Many Users on Facebook? Number of Facebook Users, January 2014. (March 2014). Retrieved August, 2014 from http://www.benphoster.com/facebook-user-growth-chart-2004-2010/Google ScholarGoogle Scholar
  24. Benjamin C. M. Fung, Ke Wang, and Philip S. Yu. 2005. Top-down specialization for information and privacy preservation. In Proceedings of the 21st International Conference on Data Engineering, 2005 (ICDE 2005). IEEE, 205--216. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Neil Zhenqiang Gong, Mario Frank, and Prateek Mittal. 2014. Sybilbelief: A semi-supervised learning approach for structure-based Sybil detection. IEEE Trans. Inform. Forens. Secur. 9, 6 (2014), 976--987. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Neil Zhenqiang Gong, Ameet Talwalkar, Lester Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Dawn Song, and others. 2011. Jointly predicting links and inferring attributes using a social-attribute network (san). arXiv preprint arXiv:1112.3265 (2011).Google ScholarGoogle Scholar
  27. Przemyslaw A. Grabowicz, José J. Ramasco, and Víctor M. Eguíluz. 2013. Dynamics in online social networks. In Dynamics On and Of Complex Networks, Volume 2. Springer, 3--17.Google ScholarGoogle Scholar
  28. Kun Guo and Qishan Zhang. 2013. Fast clustering-based anonymization approaches with time constraints for data streams. Knowl.-Based Syst. 46 (2013), 95--108. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Christopher Hope. 2013. Facebook is a ‘major location for online child sexual grooming’, head of child protection agency says. (Oct. 2013). Retrieved June, 2014 from http://www.telegraph.co.uk/technology/facebook/10380631/Facebook-is-a-major-location-for-online-child-sexual-grooming-head-of-child-protection-agency-says.html.Google ScholarGoogle Scholar
  30. Vijay S. Iyengar. 2002. Transforming data to satisfy privacy constraints. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 279--288. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Jing Jiang, Zi-Fei Shan, Xiao Wang, Li Zhang, and Ya-Fei Dai. 2015. Understanding Sybil groups in the wild. J. Comput. Sci. Technol. 30, 6 (2015), 1344--1357.Google ScholarGoogle ScholarCross RefCross Ref
  32. Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, and Shiqiang Yang. 2014. Catchsync: Catching synchronized behavior in large directed graphs. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 941--950. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Krishna B. Kansara and Narendra M. Shekokar. 2015. At a glance of Sybil detection in OSN. In Proceedings of the 2015 IEEE International Symposium on Nanoelectronic and Information Systems. IEEE, 47--52. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Kaj-Kolja Kleineberg and Marian Boguna. 2014. Trade-off between virality and mass media influence in the topological evolution of online social networks. arXiv preprint arXiv:1403.1437 (2014).Google ScholarGoogle Scholar
  35. Naeimeh Laleh, Barbara Carminati, and Elena Ferrari. 2015. Graph based local risk estimation in large scale online social networks. In Proceedings of the 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity). IEEE, 528--535.Google ScholarGoogle ScholarCross RefCross Ref
  36. Xuan Nhat Lam, Thuc Vu, Trong Duc Le, and Anh Duc Duong. 2008. Addressing cold-start problem in recommendation systems. In Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication. ACM, 208--211. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. K. Lewis. 2012. How Social Media Networks Facilitate Identity Theft and Fraud. (2012). Retrieved February, 2014 from http://www.eonetwork.org/octane-magazine/special-features/social-media-networks-facilitate-identity-theft-fraud EO Entrepreneurs Organization.Google ScholarGoogle Scholar
  38. Hongwei Li, Bo Zhao, and Ariel Fuxman. 2014. The wisdom of minority: Discovering and targeting the right group of workers for crowdsourcing. In Proceedings of the 23rd International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 165--176. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian. 2007. t-closeness: Privacy beyond k-anonymity and l-diversity. In ICDE, Vol. 7. 106--115.Google ScholarGoogle Scholar
  40. Ninghui Li, Wahbeh H. Qardaji, and Dong Su. 2011. Provably private data anonymization: Or, k-anonymity meets differential privacy. CoRR, abs/1101.2604 49 (2011), 55.Google ScholarGoogle Scholar
  41. Yixuan Li, Oscar Martinez, Xing Chen, Yi Li, and John E. Hopcroft. 2016. In a world that counts: Clustering and detecting fake social engagement at scale. In Proceedings of the 25th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 111--120. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Blerina Lika, Kostas Kolomvatsos, and Stathes Hadjiefthymiades. 2014. Facing the cold start problem in recommender systems. Expert Syst. Appl. 41, 4 (2014), 2065--2073. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Xuan Liu, Meiyu Lu, Beng Chin Ooi, Yanyan Shen, Sai Wu, and Meihui Zhang. 2012. Cdas: A crowdsourcing data analytics system. Proc. VLDB Endow. 5, 10 (2012), 1040--1051. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Ashwin Machanavajjhala, Daniel Kifer, Johannes Gehrke, and Muthuramakrishnan Venkitasubramaniam. 2007. l-diversity: Privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data (TKDD) 1, 1 (2007), 3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Mary Madden, Amanda Lenhart, Sandra Cortesi, Urs Gasser, Maeve Duggan, Aaron Smith, and Meredith Beaton. 2013. Teens, social media, and privacy. Pew Res. Center 21 (2013).Google ScholarGoogle Scholar
  46. E. Martinez. 2010. Alexis Pilkington brutally cyber bullied even after her suicide. (March 2010). Retrieved October, 2013 from http://www.cbsnews.com/8301-504083-162-20001181-504083.html CBS News.Google ScholarGoogle Scholar
  47. Merriam-Webster. 2015. Dictionary: Identity. (2015). Retrieved August 20, 2015 http://www.merriam-webster.com/dictionary/identity.Google ScholarGoogle Scholar
  48. Kimberly J. Mitchell, David Finkelhor, Lisa M. Jones, and Janis Wolak. 2010. Use of social networking sites in online sex crimes against minors: An examination of national incidence and means of utilization. J. Adolesc. Health 47, 2 (2010), 183--190.Google ScholarGoogle ScholarCross RefCross Ref
  49. Silvia Mitter, Claudia Wagner, and Markus Strohmaier. 2014. Understanding the impact of socialbot attacks in online social networks. arXiv preprint arXiv:1402.6289 (2014).Google ScholarGoogle Scholar
  50. Arvind Narayanan and Vitaly Shmatikov. 2010. Myths and fallacies of personally identifiable information. Commun. ACM 53, 6 (2010), 24--26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Róbert Ormándi, István Hegedűs, and Márk Jelasity. 2013. Gossip learning with linear models on fully distributed data. Concurr. Comput.: Pract. Exper. 25, 4 (2013), 556--571.Google ScholarGoogle ScholarCross RefCross Ref
  52. Michael Sirivianos, Kyungbaek Kim, Jian Wei Gan, and Xiaowei Yang. 2014. Leveraging social feedback to verify online identity claims. ACM Trans. Web 8, 2 (2014), 9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Rion Snow, Brendan O’Connor, Daniel Jurafsky, and Andrew Y. Ng. 2008. Cheap and fast—but is it good? Evaluating non-expert annotations for natural language tasks. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 254--263. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Amira Soliman, Leila Bahri, Barbara Carminati, Elena Ferrari, and Sarunas Girdzijauskas. 2015. DIVa: Decentralized identity validation for social networks. In Proceedings of the 2015 IEEE/ACM International Conference onAdvances in Social Networks Analysis and Mining (ASONAM). IEEE. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Gianluca Stringhini. 2014. Stepping Up the Cybersecurity Game: Protecting Online Services from Malicious Activity. Ph.D. Dissertation. University of California, Santa Barbara.Google ScholarGoogle Scholar
  56. Latanya Sweeney. 2002. k-anonymity: A model for protecting privacy. Int. J. Uncert. Fuzz. Knowl.-Based Syst. 10, 05 (2002), 557--570. Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Nguyen Tran, Jinyang Li, Lakshminarayanan Subramanian, and Sherman S. M. Chow. 2011. Optimal Sybil-resilient node admission control. In Proceedings of the 2011 IEEE INFOCOM. IEEE, 3218--3226.Google ScholarGoogle Scholar
  58. Henk C. A. Van Tilborg and Sushil Jajodia. 2011. Encyclopedia of Cryptography and Security. Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. S. Vijayarani, A. Tamilarasi, and M. Sampoorna. 2010. Analysis of privacy preserving K-anonymity methods and techniques. In Proceedings of the 2010 International Conference on Communication and Computational Intelligence (INCOCCI). IEEE, 540--545.Google ScholarGoogle Scholar
  60. Bimal Viswanath, M. Ahmad Bashir, Mark Crovella, Saikat Guha, Krishna P. Gummadi, Balachander Krishnamurthy, and Alan Mislove. 2014. Towards detecting anomalous user behavior in online social networks. In Proceedings of the 23rd USENIX Security Symposium (USENIX Security 14). 223--238. Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Gang Wang, Tristan Konolige, Christo Wilson, Xiao Wang, Haitao Zheng, and Ben Y. Zhao. 2013. You are how you click: Clickstream analysis for Sybil detection. In Proceedings of the USENIX Security Symposium. Citeseer, 1--15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. Gang Wang, Manish Mohanlal, Christo Wilson, Xiao Wang, Miriam Metzger, Haitao Zheng, and Ben Y. Zhao. 2012. Social turing tests: Crowdsourcing Sybil detection. arXiv preprint arXiv:1205.3856 (2012).Google ScholarGoogle Scholar
  63. Gang Wang, Tianyi Wang, Haitao Zheng, and Ben Y. Zhao. 2014. Man vs. machine: Practical adversarial detection of malicious crowdsourcing workers. In Proceedings of the 23rd USENIX Security Symposium (USENIX Security 14). 239--254. Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. Gang Wang, Christo Wilson, Xiaohan Zhao, Yibo Zhu, Manish Mohanlal, Haitao Zheng, and Ben Y. Zhao. 2012. Serf and turf: Crowdturfing for fun and profit. In Proceedings of the 21st International Conference on World Wide Web. ACM, 679--688. Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. Jing Wang and Panagiotis Ipeirotis. 2013. Quality-based pricing for crowdsourced workers. (2013).Google ScholarGoogle Scholar
  66. Cao Xiao, David Mandell Freeman, and Theodore Hwa. 2015. Detecting clusters of fake accounts in online social networks. In Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security. ACM, 91--101. Google ScholarGoogle ScholarDigital LibraryDigital Library
  67. Xiaokui Xiao and Yufei Tao. 2008. Dynamic anonymization: Accurate statistical analysis with privacy preservation. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. ACM, 107--120. Google ScholarGoogle ScholarDigital LibraryDigital Library
  68. Zhi Yang, Christo Wilson, Xiao Wang, Tingting Gao, Ben Y. Zhao, and Yafei Dai. 2014. Uncovering social network Sybils in the wild. ACM Trans. Knowl. Discov. Data 8, 1 (2014), 2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  69. Haifeng Yu, Phillip B. Gibbons, Michael Kaminsky, and Feng Xiao. 2008. Sybillimit: A near-optimal social network defense against Sybil attacks. In Proceedings of the IEEE Symposium on Security and Privacy, 2008 (SP 2008). IEEE, 3--17. Google ScholarGoogle ScholarDigital LibraryDigital Library
  70. Haifeng Yu, Michael Kaminsky, Phillip B. Gibbons, and Abraham Flaxman. 2006. Sybilguard: Defending against Sybil attacks via social networks. In ACM SIGCOMM Computer Communication Review, Vol. 36. ACM, 267--278. Google ScholarGoogle ScholarDigital LibraryDigital Library
  71. Daqiang Zhang, Qin Zou, and Haoyi Xiong. 2013. CRUC: Cold-start recommendations using collaborative filtering in internet of things. arXiv preprint arXiv:1306.0165 (2013).Google ScholarGoogle Scholar
  72. Kuan Zhang, Xiaohui Liang, Rongxing Lu, and Xuemin Shen. 2014. Sybil attacks and their defenses in the internet of things. IEEE IOT J. 1, 5 (2014), 372--383.Google ScholarGoogle Scholar

Index Terms

  1. COIP—Continuous, Operable, Impartial, and Privacy-Aware Identity Validity Estimation for OSN Profiles

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          cover image ACM Transactions on the Web
          ACM Transactions on the Web  Volume 10, Issue 4
          December 2016
          169 pages
          ISSN:1559-1131
          EISSN:1559-114X
          DOI:10.1145/3017848
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          Publication History

          • Published: 13 December 2016
          • Accepted: 1 September 2016
          • Revised: 1 July 2016
          • Received: 1 September 2014
          Published in tweb Volume 10, Issue 4

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