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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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
Ranjbar, A., Maheswaran, M.: Using community structure to control information sharing in online social networks. Comput. Commun. 41, 11–21 (2014)
Li, Y., Li, Y., Yan, Q., Deng, R.H.: Privacy leakage analysis in online social networks. Comput. Secur. 49, 239–254 (2015)
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)
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
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)
Benevenuto, F., et al.: Detecting spammers on Twitter. In: Collaboration, Electronic Messaging, Anti-abuse and Spam Conference (CEAS), vol. 6 (2010)
Zheng, X., et al.: Detecting spammers on social networks. Neurocomputing 159, 27–34 (2015)
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)
Cohen, Y., Gordon, D., Hendler, D.: Early detection of spamming accounts in large-scale service provider networks. Knowl. Based Syst. 142, 241–255 (2017)
Cohen, Y., Hendler, D., Rubin, A.: Detection of malicious webmail attachments based on propagation patterns. Knowl. Based Syst. 141, 67–79 (2018)
Ford, L.R., Fulkerson, D.R.: Maximal flow through a network. Can. J. Math. 8, 399–404 (1956)
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
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)
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-94147-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94146-2
Online ISBN: 978-3-319-94147-9
eBook Packages: Computer ScienceComputer Science (R0)