Fake identities and identity theft are issues whose relevance is increasing in the social network domain. This paper deals with this problem by proposing an innovative approach which combines a collaborative mechanism implementing a trust graph with keystroke-dynamic-recognition techniques to trust identities. The trust of each node is computed on the basis of neighborhood recognition and behavioral biometric support. The model leverages the word of mouth propagation and a settable degree of redundancy to obtain robustness. Experimental results show the benefit of the proposed solution even if attack nodes are present in the social network.
Contrasting false identities in social networks by trust chains and biometric reinforcement / F. Buccafurri, G. Lax, D. Migdal, S. Nicolazzo, A. Nocera, C. Rosenberger - In: 2017 International Conference on Cyberworlds (CW)[s.l] : IEEE, 2017. - ISBN 978-1-5386-2089-2. - pp. 17-24 (( convegno CW tenutosi a Chester nel 2017 [10.1109/CW.2017.42].
Contrasting false identities in social networks by trust chains and biometric reinforcement
S. Nicolazzo;
2017
Abstract
Fake identities and identity theft are issues whose relevance is increasing in the social network domain. This paper deals with this problem by proposing an innovative approach which combines a collaborative mechanism implementing a trust graph with keystroke-dynamic-recognition techniques to trust identities. The trust of each node is computed on the basis of neighborhood recognition and behavioral biometric support. The model leverages the word of mouth propagation and a settable degree of redundancy to obtain robustness. Experimental results show the benefit of the proposed solution even if attack nodes are present in the social network.File | Dimensione | Formato | |
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