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An Information-Flow Control Model for Online Social Networks Based on User-Attribute Credibility and Connection-Strength Factors

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Cyber Security Cryptography and Machine Learning (CSCML 2018)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10879))

Abstract

During the last couple of years there have been many researches on Online Social Networks (OSN). The common manner of representing an OSN is by a user-based graph, where the vertices are different OSN users, and the edges are different interactions between these users, such as friendships, information-sharing instances, and other connection types. The question of whether a certain user is willing to share its information to other users, known and less known, is a question that occupies several researches in aspects of information security, sharing habits and information-flow models for OSN. While many approaches take into consideration the OSN graph edges as sharing-probability factors, here we present a novel approach, that also combines the vertices as well-defined attributed entities, that contain several properties, in which we seek a certain level of credibility based on the user’s attributes, such as number of total friends, age of user account, etc. The edges in our model represent the connection-strength of two users, by taking into consideration the attributes that represent their connection, such as number of mutual friend, friendship duration, etc. and the model also recognizes resemblance factors, meaning the number of similar user attributes. This approach optimizes the evaluation of users’ information-sharing willingness by deriving it from these attributes, thus creating an accurate flow-control graph that prevents information leakage from users to unwanted entities, such as adversaries or spammers. The novelty of the model is mainly its choice of integrated factors for user credibility and connection credibility, making it very useful for different OSN flow-control decisions and security permissions.

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References

  1. Levy, S., Gudes, E., Gal-Oz, N.: Sharing-habits based privacy control in social networks. In: Ranise, S., Swarup, V. (eds.) DBSec 2016. LNCS, vol. 9766, pp. 217–232. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41483-6_16

    Chapter  Google Scholar 

  2. Ranjbar, A., Maheswaran, M.: Using community structure to control information sharing in online social networks. Comput. Commun. 41, 11–21 (2014)

    Article  Google Scholar 

  3. Li, Y., Li, Y., Yan, Q., Deng, R.H.: Privacy leakage analysis in online social networks. Comput. Secur. 49, 239–254 (2015)

    Article  Google Scholar 

  4. Bokobza, Y., Paradise, A., Rapaport, G., Puzis, R., Shapira, B., Shabtai, A.: Leak sinks: the threat of targeted social eavesdropping. In: 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 375–382. IEEE (2015)‏

    Google Scholar 

  5. Patil, V.T., Shyamasundar, R.K.: Undoing of privacy policies on Facebook. In: Livraga, G., Zhu, S. (eds.) DBSec 2017. LNCS, vol. 10359, pp. 239–255. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61176-1_13

    Chapter  Google Scholar 

  6. Misra, G., Such, J.M., Balogun, H.: Improve-identifying minimal profile vectors for similarity-based access control. In: 2016 IEEE Trustcom/BigDataSE/I SPA, pp. 868–875. IEEE (2016)

    Google Scholar 

  7. Benevenuto, F., et al.: Detecting spammers on Twitter. In: Collaboration, Electronic Messaging, Anti-abuse and Spam Conference (CEAS), vol. 6 (2010)‏

    Google Scholar 

  8. Zheng, X., et al.: Detecting spammers on social networks. Neurocomputing 159, 27–34 (2015)

    Article  Google Scholar 

  9. Han Veiga, M., Eickhoff, C.: A cross-platform collection of social network profiles. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2016)‏

    Google Scholar 

  10. Cohen, Y., Gordon, D., Hendler, D.: Early detection of spamming accounts in large-scale service provider networks. Knowl. Based Syst. 142, 241–255 (2017)‏

    Article  Google Scholar 

  11. Cohen, Y., Hendler, D., Rubin, A.: Detection of malicious webmail attachments based on propagation patterns. Knowl. Based Syst. 141, 67–79 (2018)

    Article  Google Scholar 

  12. Ford, L.R., Fulkerson, D.R.: Maximal flow through a network. Can. J. Math. 8, 399–404 (1956)

    Article  MathSciNet  Google Scholar 

  13. Edmonds, J., Karp, R.M.: Theoretical improvements in algorithmic efficiency for network flow problems. J. ACM. 19(2), 248–264 (1972). Association for Computing Machinery

    Article  Google Scholar 

  14. Dinic, Y.: Algorithm for solution of a problem of maximum flow in a network with power estimation. Doklady Akademii nauk SSSR 11, 1277–1280 (1970)

    Google Scholar 

  15. Benesty, J., et al.: Pearson correlation coefficient. In: Cohen, I., Huang, Y., Chen, J., Benesty, J.: Noise Reduction in Speech Processing, pp. 1–4. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00296-0_5

    Google Scholar 

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Correspondence to Nadav Voloch .

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Gudes, E., Voloch, N. (2018). An Information-Flow Control Model for Online Social Networks Based on User-Attribute Credibility and Connection-Strength Factors. In: Dinur, I., Dolev, S., Lodha, S. (eds) Cyber Security Cryptography and Machine Learning. CSCML 2018. Lecture Notes in Computer Science(), vol 10879. Springer, Cham. https://doi.org/10.1007/978-3-319-94147-9_5

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  • DOI: https://doi.org/10.1007/978-3-319-94147-9_5

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  • Online ISBN: 978-3-319-94147-9

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