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Cognitive Identity Management: Risks, Trust and Decisions using Heterogeneous Sources

Yanushkevich, Svetlana, Howells, Gareth, Crockett, Keely, O'Shea, Jim, Oliveira, H.C.R, Guest, Richard, Shmerko, Vlad (2020) Cognitive Identity Management: Risks, Trust and Decisions using Heterogeneous Sources. In: Proceedings of the First IEEE International Conference on Cognitive Machine Intelligence. Proceedings of the First IEEE International Conference on Cognitive Machine Intelligence. . IEEE ISBN 978-1-72816-738-1. E-ISBN 978-1-72816-737-4. (doi:10.1109/CogMI48466.2019.00014) (KAR id:79327)

Abstract

This work advocates for cognitive biometric-enabled systems that integrate identity management, risk assessment and trust assessment. The cognitive identity management process is viewed as a multi-state dynamical system, and probabilistic reasoning is used for modeling of this process. This paper describes an approach to design a platform for risk and trust modeling and evaluation in the cognitive identity management built upon processing heterogeneous data including biometrics, other sensory data and digital ID. The core of an approach is the perception-action cycle of each system state. Inference engine is a causal network that uses various uncertainty metrics and reasoning mechanisms including Dempster-Shafer and Dezert- Smarandache beliefs.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/CogMI48466.2019.00014
Uncontrolled keywords: biometrics, trust, cybersecurity
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics > TK7880 Applications of electronics > TK7882.B56 Biometric identification
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Gareth Howells
Date Deposited: 17 Dec 2019 11:30 UTC
Last Modified: 09 Dec 2022 02:38 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/79327 (The current URI for this page, for reference purposes)

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