Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
CADIVa: Cooperative and Adaptive Decentralized Identity Validation Model for Social Networks
KTH, School of Electrical Engineering (EES), Communication Networks.ORCID iD: 0000-0002-0264-8762
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0003-4516-7317
Show others and affiliations
2016 (English)In: Social Network Analysis and Mining, ISSN 1869-5450, E-ISSN 1869-5469, Vol. 6, no 1, article id UNSP 36Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Springer, 2016. Vol. 6, no 1, article id UNSP 36
Keyword [en]
Identity validation, Online social networks, Distributed systems, Privacy preservation, Decentralized online social networks
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
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
Projects
iSocial
Note

QC 20161003

Available from: 2016-10-03 Created: 2016-09-30 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
Keyword
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

Open Access in DiVA

fulltext(1479 kB)2 downloads
File information
File name FULLTEXT01.pdfFile size 1479 kBChecksum SHA-512
9c50d2f428205b3412137125a82358340b1407cdeebdfc104e0e5f4c57d929a7694b17dc8a6584efa86949bed8c8257473b1236d6c5727f5c0778f5be997fcbb
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Soliman, AmiraGirdzijauskas, Šarunas
By organisation
Communication NetworksSoftware and Computer systems, SCS
In the same journal
Social Network Analysis and Mining
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 2 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 62 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf