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

Yanushkevich, SN, Howells, WG, Crockett, Keeley ORCID: https://orcid.org/0000-0003-1941-6201, O'Shea, James ORCID: https://orcid.org/0000-0001-5645-2370, Oliveira, HCR de, Guest, RM and Shmerko, VP (2020) Cognitive Identity Management: Risks, Trust and Decisions using Heterogeneous Sources. In: 2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI), 12 December 2019 - 14 December 2019, Los Angeles, California, USA.

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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.