TAPESTRY: Visualizing Interwoven Identities for Trust Provenance

Yifan Yang, John Collomosse, Arthi Manohar, Jo Briggs, Jamie Steane

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)
272 Downloads (Pure)

Abstract

In 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.
Original languageEnglish
Title of host publicationVizSec 2018
Subtitle of host publicationIEEE Symposium on Visualization for Cyber Security
PublisherIEEE
Number of pages4
ISBN (Electronic)9781538681947
ISBN (Print)9781538681954
DOIs
Publication statusPublished - 22 Oct 2018

Publication series

NameIEEE VizSec
PublisherIEEE
Volume2018
ISSN (Print)2639-4359
ISSN (Electronic)2639-4332

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