Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/17970
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYang, Y-
dc.contributor.authorCollomosse, J-
dc.contributor.authorManohar, A-
dc.contributor.authorBriggs, J-
dc.contributor.authorSteane, J-
dc.coverage.spatialBerlin, Germany-
dc.date.accessioned2019-04-30T10:36:03Z-
dc.date.available2019-04-30T10:36:03Z-
dc.date.issued2018-
dc.identifier.citationVizSec 2018 - 15th IEEE Symposium on Visualization for Cyber Security, 22nd October 2018, Berlin, Germany.en_US
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/17970-
dc.description© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.description.abstractIn this paper we report our study involving an early prototype of TAPESTRY, a service to support people and businesses to connect safely online through the use of a Machine Learning generated visualization. Establishing the veracity of the person or business behind a pseudonomized identity, online, is a challenge for many people. In the burgeoning digital economy, finding ways to support good decision-making in potentially risky online exchanges is of vital importance. In this paper, we propose a Machine Learning method to extract temporal patterns from data on individuals’ behavioural norms in their online activity. This monitors and communicates the coherence of these activities to others, especially those who are about to disclose personal information to the individual, in a visualization. We report findings from a user trial that examined how people accessed and interpreted the TAPESTRY visualization to inform their decisions on who to back in a mock crowdfunding campaign to evaluate its efficacy. The study proved the protocol of the Machine Learning method and qualitative insights are informing iterations of the visualization design to enhance user experience and support understanding.en_US
dc.description.sponsorshipThis work is funded by EPSRC project grant REF: EP/N02799X/1.en_US
dc.language.isoenen_US
dc.sourceVizSec 2018 - 15th IEEE Symposium on Visualization for Cyber Security,-
dc.sourceVizSec 2018 - 15th IEEE Symposium on Visualization for Cyber Security,-
dc.subjectmachine learningen_US
dc.subjecttopic modelingen_US
dc.subjectlong short term memoryen_US
dc.subjecthuman computer interactionen_US
dc.subjectusability testingen_US
dc.titleTAPESTRY: Visualizing Interwoven Identities for Trust Provenanceen_US
dc.typeConference Paperen_US
pubs.publication-statusPublished-
Appears in Collections:Brunel Design School Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdf1.53 MBAdobe PDFView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.