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Cognitive Identity Management: Synthetic Data, Risk and Trust

Yanushkevich, Svetlana, Stoica, Adrian, Shmerko, Vlad, Howells, Gareth, Crockett, Keely, Guest, Richard (2020) Cognitive Identity Management: Synthetic Data, Risk and Trust. In: 2020 International Joint Conference on Neural Networks (IJCNN). Proceedings 2020 International Joint Conference on Neural Networks (IJCNN). . IEEE ISBN 978-1-72816-926-2. (doi:10.1109/IJCNN48605.2020.9207385) (KAR id:80887)


Synthetic, or artificial data is used in security applications such as protection of sensitive information, prediction of rare events, and training neural networks. Risk and trust are assessed specifically for a given kind of synthetic data and particular application. In this paper, we consider a more complicated scenario, – biometric-enabled cognitive cognitive biometric-enabled identity management, in which multiple kinds of synthetic data are used in addition to authentic data. For example, authentic biometric traits can be used to train the intelligent tools to identify humans, while synthetic, algorithmically generated data can be used to expand the training set or to model extreme situations. This paper is dedicated to understanding the potential impact of synthetic data on the cognitive checkpoint performance, and risk and trust prediction.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/IJCNN48605.2020.9207385
Uncontrolled keywords: Synthetic data, cognitive identity management, risk, trust, bias, computational intelligence
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Gareth Howells
Date Deposited: 17 Apr 2020 16:40 UTC
Last Modified: 22 Feb 2022 16:51 UTC
Resource URI: (The current URI for this page, for reference purposes)

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