Visible to the public "DIVa: Decentralized identity validation for social networks"Conflict Detection Enabled

Title"DIVa: Decentralized identity validation for social networks"
Publication TypeConference Paper
Year of Publication2015
AuthorsA. Soliman, L. Bahri, B. Carminati, E. Ferrari, S. Girdzijauskas
Conference Name2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Date PublishedAug
ISBN Number978-1-4503-3854-7
Accession Number15775491
Keywordsassociation rules, Community-aware Identity Validation, Computational modeling, Correlation, data mining, data privacy, decentralized identity validation, decentralized learning approach, Decentralized Online Social Networks, DIVa, Ensemble Learning, fine-grained community-aware correlation, learning (artificial intelligence), mining technique, online social network, privacy, privacy preservation, Privacy-preserving Learning, pubcrawl170105, real-world OSN dataset, security, Social network services, social networking (online), user profile attribute

Online Social Networks exploit a lightweight process to identify their users so as to facilitate their fast adoption. However, such convenience comes at the price of making legitimate users subject to different threats created by fake accounts. Therefore, there is a crucial need to empower users with tools helping them in assigning a level of trust to whomever they interact with. To cope with this issue, in this paper we introduce a novel model, DIVa, that leverages on mining techniques to find correlations among user profile attributes. These correlations are discovered not from user population as a whole, but from individual communities, where the correlations are more pronounced. DIVa exploits a decentralized learning approach and ensures privacy preservation as each node in the OSN independently processes its local data and is required to know only its direct neighbors. Extensive experiments using real-world OSN datasets show that DIVa is able to extract fine-grained community-aware correlations among profile attributes with average improvements up to 50% than the global approach.

Citation Key7403568