Abstract
This paper discusses how Sybil attacks can undermine trust management systems and how to respond to these attacks using advanced techniques such as credibility and probabilistic databases. In such attacks end-users have purposely different identities and hence, can provide inconsistent ratings over the same Web Services. Many existing approaches rely on arbitrary choices to filter out Sybil users and reduce their attack capabilities. However this turns out inefficient. Our approach relies on non-Sybil credible users who provide consistent ratings over Web services and hence, can be trusted. To establish these ratings and debunk Sybil users techniques such as fuzzy-clustering, graph search, and probabilistic databases are adopted. A series of experiments are carried out to demonstrate robustness of our trust approach in presence of Sybil attacks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Bezdek, J.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell (1981)
Bordens, K., Horowitz, I.: Social Psychology. Psychology Press, Mahwah (2001)
Buchegger, S., Boudec, J.Y.L.: A robust reputation system for peer-to-peer and mobile ad-hoc networks. In: P2P Econ, Cambridge, USA (2004)
Cavallo, R., Pittarelli, M.: The theory of probabilistic databases. In: Very Large Data Bases Conferences, Brighton, England (1987)
Cheng, A., Friedman, E.: Sybilproof reputation mechanisms. In: Proceedings of the 2005 ACM SIGCOMM Workshop on Economics of Peer-to-peer Systems. ACM, New York, NY, USA (2005)
Dalvi, N., Suciu, D.: Efficient query evaluation on probabilistic databases. VLDB J. 16(4), 523–544 (2007)
Danezis, G., Mittal, P.: Sybilinfer: detecting sybil nodes using social networks. Technical report (2009)
Ford, L.R., Fulkerson, D.R.: A simple algorithm for finding maximal network flows and an application to the hitchcock problem. Can. J. Math. 9(2), 210–218 (1957)
Fuhr, N., Rölleke, T.: A probabilistic relational algebra for the integration of information retrieval and database systems. ACM Tran. Inf. Syst. (TOIS) 15(1), 32–66 (1997)
Hota, C., Srikanth, M., Yla-Jaaski, A., Lindqvist, J., Kristiina, K.: Safeguarding against sybil attacks via social networks and multipath routing. In: 2007 Second International Conference on Communications and Networking in China, CHINACOM 2007 (2007)
Jayram, T.S., Kale, S., Vee, E.: Efficient aggregation algorithms for probabilistic data. In: Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, USA (2007)
Lesko, W.: Readings in Social Psychology: General, Classic and Contemporary Selections. Allyn & Bacon, Boston (1997)
Mihail, M., Saberi, P.A.: Random walks with lookahead in power law random graphs. Internet Math. 1(1), 147–152 (2007)
Malik, Z., Bouguettaya, A.: Rateweb: reputation assessment for trust establishment among web services. Very Large Data Bases (VLDB) J 18(4), 885–911 (2009)
Mislove, A., Viswanath, B., Gummadi, K., Druschel, P.: You are who you know: inferring user profiles in online social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining. WSDM 2010 (2010)
Nguyen, N., Caruana, R.: Consensus clusterings. In: International Conference on Data Mining, Omaha, USA (2007)
Noor, T., Sheng, Q., Ngu, A., Alfazi, A., Law, J.: Cloud armor: a platform for credibility-based trust management of cloud services. In: The ACM Conference on Information and Knowledge Management (CIKM) (2013)
Saoud, Z., Faci, N., Maamar, Z., Benslimane, D.: A fuzzy clustering-based credibility model for trust assessment in a service-oriented architecture. In: International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), Parma, Italy (2014)
Sarma, A., Benjelloun, O., Halevy, A., Widom, J.: Working models for uncertain data. In: International Conference on Data Engineering (ICDE), Atlanta, USA (2006)
Schum, D., Morris, J.: Assessing the competence and credibility of human sources of intelligence evidence: contributions from law and probability. Law Probab. Risk 6(1), 247–274 (2007)
Sternthal, B., Phillips, L., Dholakia, R.: The persuasive effect of source credibility: a situational analysis. Public Opin. Q. 42(3), 285–314 (1978)
Suciu, D.: Probabilistic databases. SIGACT News 39(2), 17–43 (2008)
Teacy, W.T., Patel, J., Jennings, N.R., Luck, M.: Travos: trust and reputation in the context of inaccurate information sources. Auton. Agent. Multi-Agent Syst. 12(2), 239–256 (2006)
Tran, N., Min, B., Li, J., Subramanian, L.: Sybil-resilient online content voting. In: Proceedings of the 6th USENIX Symposium on Networked Systems Design and Implementation, Berkeley, CA, USA (2009)
Troffaes, M.: Generalizing the conjunction rule for aggregating conflicting expert opinions. Int. J. Intell. Syst. 21(3), 229–259 (2006)
Viswanath, B., Mondal, M., Gummadi, K., Mislove, A., Post, A.: Canal: scaling social network-based Sybil tolerance schemes. In: Proceedings of the 7th European Conference on Computer Systems (EuroSys 2012), Bern, Switzerland (2012)
Vu, L.-H., Hauswirth, M., Aberer, K.: QoS-based service selection and ranking with trust and reputation management. In: Meersman, R., Tari, Z. (eds.) OTM 2005. LNCS, vol. 3760, pp. 466–483. Springer, Heidelberg (2005)
Wang, Y., Singh, M.: Formal trust model for multiagent systems. In: Proceedings of the International Joint Conference on Artifical Intelligence, Hyderabad, India (2007)
Yu, H., Gibbons, P.B., Kaminsky, M., Xiao, F.: Sybillimit: A near-optimal social network defense against sybil attacks. In: Proceedings of the 2008 IEEE Symposium on Security and Privacy. IEEE Computer Society, Washington, DC, USA (2008)
Yu, H., Kaminsky, M., Gibbons, P.B., Flaxman, A.: Sybilguard: defending against sybil attacks via social networks. SIGCOMM Comput. Commun. Rev. 36, 267–278 (2006)
Zhou, R., Hwang, K.: Powertrust: a robust and scalable reputation system for trusted peer-to-peer computing. IEEE Trans. Parallel Distrib. Syst. 18(4), 460–473 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Saoud, Z., Faci, N., Maamar, Z., Benslimane, D. (2015). Sybil Tolerance and Probabilistic Databases to Compute Web Services Trust. In: Tadeusz, M., Valduriez, P., Bellatreche, L. (eds) Advances in Databases and Information Systems. ADBIS 2015. Lecture Notes in Computer Science(), vol 9282. Springer, Cham. https://doi.org/10.1007/978-3-319-23135-8_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-23135-8_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23134-1
Online ISBN: 978-3-319-23135-8
eBook Packages: Computer ScienceComputer Science (R0)