Conference paper
Understanding victim-enabled identity theft
2016 Fourteenth Annual International Conference on Privacy, Security and Trust, pp.196-202
Annual Conference on Privacy, Security and Trust (PST), 14th (Auckland, New Zealand, 12-Dec-2016–14-Dec-2016)
IEEE Publishing Inc.
2017
Abstract
Victim-enabled identity theft is a crime in which an individual victim is deceived into providing their personally identifying information (PII) to a criminal to facilitate its theft and/or misuse. In this paper we analyse a particular victim-enabled tax-related identity theft scheme recently reported in Australia, which has also been reported, in a slightly different guise, in the US. We find that this scheme, and others like it, are best understood when studied from both the perpetrator's and the victim's points of view. The criminal perspective and business practices have been captured and analysed in the Identity Threat Assessment and Prediction (ITAP) model developed by the Center for Identity at The University of Texas (UT CID). The victim perspective has been captured from multiple victim case files captured by IDCARE. The research findings support the view that combining perspectives enhances the analytical value of a threat assessment and prediction model. The multi-actor nature of victim-enabled identity theft complements the methodological approach adopted in the paper, and provides new insights on a growing form of identity theft that can inform future prevention and detection response strategies. © 2016 IEEE.
Details
- Title
- Understanding victim-enabled identity theft
- Authors
- David Lacey (Author) - University of the Sunshine Coast - Faculty of Arts, Business and LawJim Zaiss (Author) - University of Texas at Austin, United StatesK Suzanne Barber (Author) - University of Texas at Austin, United States
- Publication details
- 2016 Fourteenth Annual International Conference on Privacy, Security and Trust, pp.196-202
- Conference details
- Annual Conference on Privacy, Security and Trust (PST), 14th (Auckland, New Zealand, 12-Dec-2016–14-Dec-2016)
- Publisher
- IEEE Publishing Inc.
- Date published
- 2017
- DOI
- 10.1109/PST.2016.7906926
- ISBN
- 9781509043798
- Organisation Unit
- Cyber Institute; University of the Sunshine Coast, Queensland
- Language
- English
- Record Identifier
- 99450705102621
- Output Type
- Conference paper
Metrics
4 File views/ downloads
505 Record Views