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Social Network Analysis and Mining

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01-12-2016 | Original Article

CADIVa: cooperative and adaptive decentralized identity validation model for social networks

Authors: Amira Soliman, Leila Bahri, Sarunas Girdzijauskas, Barbara Carminati, Elena Ferrari

Published in: Social Network Analysis and Mining | Issue 1/2016

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Abstract

Online social networks (OSNs) have successfully changed the way people interact. Online interactions among people span geographical boundaries and interweave with different human life activities. However, current OSNs identification schemes lack guarantees on quantifying the trustworthiness of online identities of users joining them. Therefore, driven from the need to empower users with an identity validation scheme, we introduce a novel model, cooperative and adaptive decentralized identity validation CADIVa, that allows OSN users to assign trust levels to whomever they interact with. CADIVa exploits association rule mining approach to extract the identity correlations among profile attributes in every individual community in a social network. CADIVa is a fully decentralized and adaptive model that exploits fully decentralized learning and cooperative approaches not only to preserve users privacy, but also to increase the system reliability and to make it resilient to mono-failure. CADIVa follows the ensemble learning paradigm to preserve users privacy and employs gossip protocols to achieve efficient and low-overhead communication. We provide two different implementation scenarios of CADIVa. Results confirm CADIVa’s ability to provide fine-grained community-aware identity validation with average improvement up to 36 and 50 % compared to the semi-centralized or global approaches, respectively.

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We exploit the community detection algorithm suggested in Rahimian et al. (2014) as it provides a fully decentralized solution.

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Title: CADIVa: cooperative and adaptive decentralized identity validation model for social networks
Authors: Amira Soliman
Leila Bahri
Sarunas Girdzijauskas
Barbara Carminati
Elena Ferrari
Publication date: 01-12-2016
Publisher: Springer Vienna
Published in: Social Network Analysis and Mining / Issue 1/2016
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-016-0343-z

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