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    Cognitive Identity Management: Synthetic Data, Risk and Trust

    Yanushkevich, S, Stoica, A, Shmerko, P, Howells, W, Crockett, K ORCID logoORCID: https://orcid.org/0000-0003-1941-6201 and Guest, R (2020) Cognitive Identity Management: Synthetic Data, Risk and Trust. In: IEEE World Congress on Computational Intelligence (WCCI) 2020, 19 July 2020 - 24 July 2020, Glasgow, UK.

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    Abstract

    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 (Paper)
    Published Proceedings: 2020 International Joint Conference on Neural Networks (IJCNN)
    Peer-reviewed: Yes
    Date Deposited: 31 Mar 2020 12:56
    Publisher: IEEE
    Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Divisions: Faculties > Science and Engineering
    Research Centres > Centre for Advanced Computational Science
    Subject terms: Synthetic data, cognitive identity management, risk, trust, bias, computational intelligence
    URI: https://e-space.mmu.ac.uk/id/eprint/625463

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