DIVa: Decentralized Identity Validation for Social Networks
2015 (English)In: PROCEEDINGS OF THE 2015 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2015), Association for Computing Machinery (ACM), 2015, 383-391 p.Conference paper (Refereed)Text
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.
Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2015. 383-391 p.
Community-aware Identity Validation, Ensemble Learning, Privacy-preserving Learning, Decentralized Online Social Networks
IdentifiersURN: urn:nbn:se:kth:diva-185412DOI: 10.1145/2808797.2808861ISI: 000371793500054ScopusID: 2-s2.0-84962492143ISBN: 978-1-4503-3854-7OAI: oai:DiVA.org:kth-185412DiVA: diva2:921769
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), AUG 25-28, 2015, Paris, FRANCE
QC 201604212016-04-212016-04-182016-04-21Bibliographically approved