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DIVa: Decentralized Identity Validation for Social Networks
KTH, School of Electrical Engineering (EES), Communication Networks.ORCID iD: 0000-0002-0264-8762
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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, p. 383-391Conference paper, Published paper (Refereed)
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Text
Abstract [en]

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. p. 383-391
Keywords [en]
Community-aware Identity Validation, Ensemble Learning, Privacy-preserving Learning, Decentralized Online Social Networks
National Category
Communication Studies
Identifiers
URN: urn:nbn:se:kth:diva-185412DOI: 10.1145/2808797.2808861ISI: 000371793500054Scopus ID: 2-s2.0-84962492143ISBN: 978-1-4503-3854-7 (print)OAI: oai:DiVA.org:kth-185412DiVA, id: diva2:921769
Conference
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), AUG 25-28, 2015, Paris, FRANCE
Note

QC 20160421

Available from: 2016-04-21 Created: 2016-04-18 Last updated: 2018-02-02Bibliographically approved
In thesis
1. Graph-based Analytics for Decentralized Online Social Networks
Open this publication in new window or tab >>Graph-based Analytics for Decentralized Online Social Networks
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Decentralized Online Social Networks (DOSNs) have been introduced as a privacy preserving alternative to the existing online social networks.  DOSNs remove the dependency on a centralized provider and operate as distributed information management platforms. Current efforts of providing DOSNs are mainly focused on designing the required building blocks for managing the distributed network and supporting the social services (e.g., search, content delivery, etc.). However, there is a lack of reliable techniques for enabling complex analytical services (e.g., spam detection, identity validation, etc.) that comply with the decentralization requirements of DOSNs. In particular, there is a need for decentralized data analytic techniques and machine learning (ML) algorithms that can successfully run on top of DOSNs.

 

In this thesis, we empower decentralized analytics for DOSNs through a set of novel algorithms. Our algorithms allow decentralized analytics to effectively work on top of fully decentralized topology, when the data is fully distributed and nodes have access to their local knowledge only. Furthermore, our algorithms and methods are able to extract and exploit the latent patterns in the social user interaction networks and effectively combine them with the shared content, yielding significant improvements for the complex analytic tasks. We argue that, community identification is at the core of the learning and analytical services provided for DOSNs. We show in this thesis that knowledge on community structures and information dissemination patterns, embedded in the topology of social networks has a potential to greatly enhance data analytic insights and improve results. At the heart of this thesis lies a community detection technique that successfully extracts communities in a completely decentralized manner. In particular, we show that multiple complex analytic tasks, like spam detection and identity validation,  can be successfully tackled by harvesting the information from the social network structure. This is achieved by using decentralized community detection algorithm which acts as the main building block for the community-aware learning paradigm that we lay out in this thesis. To the best of our knowledge, this thesis represents the first attempt to bring complex analytical services, which require decentralized iterative computation over distributed data, to the domain of DOSNs. The experimental evaluation of our proposed algorithms using real-world datasets confirms the ability of our solutions to generate  efficient ML models in massively parallel and highly scalable manner.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2018. p. 41
Series
TRITA-EECS-AVL ; 2018:4
Keywords
Decentralized Community Detection, Community-aware Learning, Spam Detection, Identity Validation
National Category
Computer Systems
Research subject
Information and Communication Technology
Identifiers
urn:nbn:se:kth:diva-222228 (URN)978-91-7729-666-9 (ISBN)
Public defence
2018-03-09, sal C, Electrum building, Kistagången 16, STOCKHOLM, 09:00 (English)
Opponent
Supervisors
Note

QC 20180205

Available from: 2018-02-05 Created: 2018-02-02 Last updated: 2018-02-05Bibliographically approved

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Soliman, Amira

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