Digital Footprints: Your Unique Identity

Juanita Blue, Joan Condell, Tom Lunney

Research output: Contribution to conferencePaper

Abstract

In the digital age where Human Computer Interaction is creating large and entirely unique digital footprints, online accounts and activities can prove to be a valuable source of information that may contribute to verification that an asserted identity is genuine. Online social contextual data – or ‘Digital identities’ -- pertaining to real people are built over time and bolstered by associated accounts, relationships and attributes. This data is difficult to fake and therefore may have the capacity to provide proof of a ‘real’ identity. This paper outlines the design and initial development of a solution that utilizes data sourced from an individual’s digital footprint to assess the likelihood that it pertains to a ‘real’ identity. This is achieved through application of machine learning and Bayesian probabilistic modelling techniques.

Conference

ConferenceBCS, The Chartered Institute for IT (ACM Proceedings)
32nd Human Computer Interaction Conference
Abbreviated titleBHCI 2018
CountryUnited Kingdom
CityBelfast
Period2/07/186/07/18
Internet address

Fingerprint

Human computer interaction
Learning systems

Keywords

  • Identity
  • authentication
  • digital footprint
  • Privacy
  • Security

Cite this

Blue, J., Condell, J., & Lunney, T. (2018). Digital Footprints: Your Unique Identity . 1-4. Paper presented at BCS, The Chartered Institute for IT (ACM Proceedings)
32nd Human Computer Interaction Conference, Belfast, United Kingdom.
Blue, Juanita ; Condell, Joan ; Lunney, Tom. / Digital Footprints : Your Unique Identity . Paper presented at BCS, The Chartered Institute for IT (ACM Proceedings)
32nd Human Computer Interaction Conference, Belfast, United Kingdom.4 p.
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abstract = "In the digital age where Human Computer Interaction is creating large and entirely unique digital footprints, online accounts and activities can prove to be a valuable source of information that may contribute to verification that an asserted identity is genuine. Online social contextual data – or ‘Digital identities’ -- pertaining to real people are built over time and bolstered by associated accounts, relationships and attributes. This data is difficult to fake and therefore may have the capacity to provide proof of a ‘real’ identity. This paper outlines the design and initial development of a solution that utilizes data sourced from an individual’s digital footprint to assess the likelihood that it pertains to a ‘real’ identity. This is achieved through application of machine learning and Bayesian probabilistic modelling techniques.",
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year = "2018",
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language = "English",
pages = "1--4",
note = "BCS, The Chartered Institute for IT (ACM Proceedings)<br/>32nd Human Computer Interaction Conference, BHCI 2018 ; Conference date: 02-07-2018 Through 06-07-2018",
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Blue, J, Condell, J & Lunney, T 2018, 'Digital Footprints: Your Unique Identity ' Paper presented at BCS, The Chartered Institute for IT (ACM Proceedings)
32nd Human Computer Interaction Conference, Belfast, United Kingdom, 2/07/18 - 6/07/18, pp. 1-4.

Digital Footprints : Your Unique Identity . / Blue, Juanita; Condell, Joan; Lunney, Tom.

2018. 1-4 Paper presented at BCS, The Chartered Institute for IT (ACM Proceedings)
32nd Human Computer Interaction Conference, Belfast, United Kingdom.

Research output: Contribution to conferencePaper

TY - CONF

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T2 - Your Unique Identity

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AU - Condell, Joan

AU - Lunney, Tom

PY - 2018/7/3

Y1 - 2018/7/3

N2 - In the digital age where Human Computer Interaction is creating large and entirely unique digital footprints, online accounts and activities can prove to be a valuable source of information that may contribute to verification that an asserted identity is genuine. Online social contextual data – or ‘Digital identities’ -- pertaining to real people are built over time and bolstered by associated accounts, relationships and attributes. This data is difficult to fake and therefore may have the capacity to provide proof of a ‘real’ identity. This paper outlines the design and initial development of a solution that utilizes data sourced from an individual’s digital footprint to assess the likelihood that it pertains to a ‘real’ identity. This is achieved through application of machine learning and Bayesian probabilistic modelling techniques.

AB - In the digital age where Human Computer Interaction is creating large and entirely unique digital footprints, online accounts and activities can prove to be a valuable source of information that may contribute to verification that an asserted identity is genuine. Online social contextual data – or ‘Digital identities’ -- pertaining to real people are built over time and bolstered by associated accounts, relationships and attributes. This data is difficult to fake and therefore may have the capacity to provide proof of a ‘real’ identity. This paper outlines the design and initial development of a solution that utilizes data sourced from an individual’s digital footprint to assess the likelihood that it pertains to a ‘real’ identity. This is achieved through application of machine learning and Bayesian probabilistic modelling techniques.

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KW - authentication

KW - digital footprint

KW - Privacy

KW - Security

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Blue J, Condell J, Lunney T. Digital Footprints: Your Unique Identity . 2018. Paper presented at BCS, The Chartered Institute for IT (ACM Proceedings)
32nd Human Computer Interaction Conference, Belfast, United Kingdom.