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CADIVa: Cooperative and Adaptive Decentralized Identity Validation Model for Social Networks
KTH, Skolan för elektro- och systemteknik (EES), Kommunikationsnät.ORCID-id: 0000-0002-0264-8762
KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.ORCID-id: 0000-0003-4516-7317
Visa övriga samt affilieringar
2016 (Engelska)Ingår i: Social Network Analysis and Mining, ISSN 1869-5450, E-ISSN 1869-5469, Vol. 6, nr 1, artikel-id UNSP 36Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

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.

Ort, förlag, år, upplaga, sidor
Springer, 2016. Vol. 6, nr 1, artikel-id UNSP 36
Nyckelord [en]
Identity validation, Online social networks, Distributed systems, Privacy preservation, Decentralized online social networks
Nationell ämneskategori
Elektroteknik och elektronik
Identifikatorer
URN: urn:nbn:se:kth:diva-193150DOI: 10.1007/s13278-016-0343-zISI: 000381220500036Scopus ID: 2-s2.0-84976332626OAI: oai:DiVA.org:kth-193150DiVA, id: diva2:1014949
Projekt
iSocial
Anmärkning

QC 20161003

Tillgänglig från: 2016-10-03 Skapad: 2016-09-30 Senast uppdaterad: 2018-02-02Bibliografiskt granskad
Ingår i avhandling
1. Graph-based Analytics for Decentralized Online Social Networks
Öppna denna publikation i ny flik eller fönster >>Graph-based Analytics for Decentralized Online Social Networks
2018 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
KTH Royal Institute of Technology, 2018. s. 41
Serie
TRITA-EECS-AVL ; 2018:4
Nyckelord
Decentralized Community Detection, Community-aware Learning, Spam Detection, Identity Validation
Nationell ämneskategori
Datorsystem
Forskningsämne
Informations- och kommunikationsteknik
Identifikatorer
urn:nbn:se:kth:diva-222228 (URN)978-91-7729-666-9 (ISBN)
Disputation
2018-03-09, sal C, Electrum building, Kistagången 16, STOCKHOLM, 09:00 (Engelska)
Opponent
Handledare
Anmärkning

QC 20180205

Tillgänglig från: 2018-02-05 Skapad: 2018-02-02 Senast uppdaterad: 2018-02-05Bibliografiskt granskad

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