Journal of Communications and Networks

  • 제17권2호
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  • Pages.145-156
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  • 2015
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  • 1229-2370(pISSN)
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  • 1976-5541(eISSN)
한국통신학회 (The Korean Institute of Commucations and Information Sciences)
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DOI QR코드

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Toward Trustworthy Social Network Services: A Robust Design of Recommender Systems

  • Noh, Giseop (Dept. of Computer Science and Engineering, Seoul National University) ;
  • Oh, Hayoung (School of Electronic and Engineering, Soongsil University) ;
  • Lee, Kyu-haeng (Dept. of Computer Science and Engineering, Seoul National University) ;
  • Kim, Chong-kwon (Dept. of Computer Science and Engineering, Seoul National University)
  • 투고 : 2014.08.28
  • 발행 : 2015.04.30
⟨ 이전 논문 다음 논문 ⟩

초록

In recent years, electronic commerce and online social networks (OSNs) have experienced fast growth, and as a result, recommendation systems (RSs) have become extremely common. Accuracy and robustness are important performance indexes that characterize customized information or suggestions provided by RSs. However, nefarious users may be present, and they can distort information within the RSs by creating fake identities (Sybils). Although prior research has attempted to mitigate the negative impact of Sybils, the presence of these fake identities remains an unsolved problem. In this paper, we introduce a new weighted link analysis and influence level for RSs resistant to Sybil attacks. Our approach is validated through simulations of a broad range of attacks, and it is found to outperform other state-of-the-art recommendation methods in terms of both accuracy and robustness.

키워드

과제정보

연구 과제 주관 기관 : National Research Foundation of Korea (NRF)

참고문헌

  1. J. Douceur, "The Sybil Attack" in Peer-to-peer Systems, Germany, Heidelberg: Springer, 2002, pp. 251-260.
  2. B. Mehta, T. Hofmann, and P. Fankhauser, "Lies and propaganda: Detecting spam users in collaborative filtering," in Proc. 12th Int. Conf. IUI, 2007, pp. 14-21.
  3. B. Mehta and W. Nejdl, "Attack resistant collaborative filtering," in Proc. 31st Annu. Int. ACM SIGIR Conf. Research and Development in Inform. Retrieval, 2008, pp. 75-82.
  4. I. T. Jolliffe. Principal Component Analysis, 2nd Ed. Springer, 2002.
  5. Y. Koren, "Factorization meets the neighborhood: A multifaceted collaborative filtering model," in Proc. 14th ACM SIGKDD Int. Conf. Knowl. Discovery and Data Mining, Aug. 2008, pp. 426-434.
  6. Z. Cheng and N. Hurley, "Robust collaborative recommendation by least trimmed squares matrix factorization," in Proc. 22nd IEEE Int. Conf. ICTAI, 2010, pp. 105-112.
  7. J. M. Kleinberg, "Authoritative sources in a hyperlinked environment," J. ACM, vol. 46, pp. 604-632, 1999. https://doi.org/10.1145/324133.324140
  8. B.Mobasher et al., "Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness," ACM Trans. Internet Technol., vol. 7, p. 23, 2007. https://doi.org/10.1145/1278366.1278372
  9. P. Melville and V. Sindhwani, "Recommender systems," Encyclopedia of machine learning, pp. 829-838, 2010.
  10. P. Resnick et al., "GroupLens: An open architecture for collaborative filtering of netnews," in Proc. ACM conf. Comput. Supported Cooperative Work, 1994, pp. 175-186.
  11. G. Linden, B. Smith, and J. York, "Amazon.com recommendations: Itemto-item collaborative filtering," IEEE Internet Comput., vol. 7, pp. 76-80, 2003. https://doi.org/10.1109/MIC.2003.1167344
  12. Y. Koren, R. Bell, and C. Volinsky, "Matrix factorization techniques for recommender systems," Computer, vol. 42, pp. 30-37, 2009.
  13. P. Melville and V. Sindhwani, "Recommender systems," Encyclopedia of machine learning, pp. 829-838, 2010.
  14. K. Lang, "NewsWeeder: Learning to filter netnews," in Proc. 12th Int. Conf. Mach. Learn., San Mateo, CA, USA, 1995, pp. 331-339.
  15. P. Melville, R. J. Mooney, and R. Nagarajan, "Content-boosted collaborative filtering for improved recommendations," in Proc. Nat. Conf. Artificial Intell., 2002, pp. 187-192.
  16. Pujahari, Abinash, and Vineet Padmanabhan, "A New Grouping Method Based on Social Choice Strategies for Group Recommender System," Computational Intelligence in Data Mining-Volume 1. Springer India, 2015. pp. 325-332.
  17. H. F. Yu et al., "SybilGuard: Defending against sybil attacks via social networks," IEEE-ACM Trans. Netw., vol. 16, no. 3, pp. 576-589, June 2008. https://doi.org/10.1109/TNET.2008.923723
  18. H. F. Yu et al., "SybilLimit: A near-optimal social network defense against Sybil attacks," IEEE-ACM Trans. Netw., vol. 18, pp. 885-898, June 2010. https://doi.org/10.1109/TNET.2009.2034047
  19. N. Tran et al., "Sybil-resilient online content voting," in Proc. 6th USENIX Symp. on Netw. Syst. Design and Implementation, 2009, pp. 15-28.
  20. N. Tran et al., "Optimal sybil-resilient node admission control," in Proc. IEEE INFOCOM, 2011, pp. 3218-3226.
  21. W. Wei et al., "Sybildefender: Defend against sybil attacks in large social networks," in Proc. IEEE INFOCOM, 2012, pp. 1951-1959.
  22. Z. Gyongyi, H. Garcia-Molina, and J. Pedersen, "Combating web spam with trustrank," in Proc. 30th Int. Conf. Very Large Data Bases, vol. 30, 2004, pp. 576-587.
  23. M. Sobek. (2002). PR0 - Google's PageRank 0 Penalty [Online]. Available: http://pr.efactory.de/e-pr0.shtml
  24. S. Ghosh et al., "Understanding and combating link farming in the twitter social network," in Proc. 21st Int. Conf. World Wide Web, 2012, pp. 61-70.
  25. C. Yang et al., "Analyzing spammers' social networks for fun and profit: A case study of cyber criminal ecosystem on twitter," in Proc. 21st Int. Conf. World Wide Web, 2012, pp. 71-80.
  26. N. Z. Gong, M. Frank, and P. Mittal, "SybilBelief: A semi-supervised learning approach for structure-based Sybil detection," IEEE Trans. Inform. Forensics Security, 2014.
  27. Egele et al., "COMPA: Detecting ompromised accounts on social networks," NDSS. 2013.
  28. J. Meng et al., "Inferring Strange Behavior from Connectivity Pattern in Social Networks," Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2014. pp. 126-138.
  29. H. Yu et al., "Dsybil: Optimal sybil-resistance for recommendation systems," in Proc. 30th IEEE Symp. Security and Privacy, 2009, pp. 283-298.
  30. G. Noh et al., "Robust Sybil attack defense with information level in online recommender systems," Expert Systems with Applications. 2014;41(4, Part 2):1781-1791. https://doi.org/10.1016/j.eswa.2013.08.077
  31. B. Mobasher, R. Burke, and J. J. Sandvig, "Model-based collaborative filtering as a defense against profile injection attacks," in Proc. Nat. Conf. Artificial Intell., 2006, p. 1388.
  32. M. Jamali and M. Ester, "A matrix factorization technique with trust propagation for recommendation in social networks," in Proc. 4th ACM Conf. Recommender Syst., 2010, pp. 135-142.
  33. V. R. Kagita, A. K. Pujari, and V. Padmanabhan, "Virtual user approach for group recommender systems using precedence relations," Information Sciences 294 (2015): 15-30. https://doi.org/10.1016/j.ins.2014.08.072
  34. Mislove et al., "Measurement and analysis of online social networks," in Proc. 7th ACM SIGCOMM Conf. Internet Measurement, 2007.
  35. Clauset et al., "Power-law distributions in empirical data," SIAM review 51.4 (2009): 661-703. https://doi.org/10.1137/070710111
  36. G. Noh et al., "PSD: Practical Sybil Detection Schemes Using Stickiness and Persistence in Online Recommender Systems," Information Sciences, 2014.
  37. N. Lathia, S. Hailes, and L. Capra, "Temporal defenses for robust recommendations," in Proc. PSDML, Barcelona, Spain, 2011.