Compliance through Informed Consent: Semantic Based Consent Permission and Data Management Model

Kaniz Fatema, Ensar Hadziselimovic, Harshvardhan Pandit, Christophe Debruyne, Dave Lewis, Declan O'Sullivan

    Research output: Contribution to journalConference articlepeer-review


    The General Data Protection Regulations (GDPR) imposes greater
    restrictions on obtaining valid user consents involving the use of personal data.
    A semantic model of consent can make the concepts of consent explicit, establish a common understanding and enable re-use of consent. Therefore, forming
    a semantic model of consent will satisfy the GDPR requirements of specificity
    and unambiguity and is an important step towards ensuring compliance. In this
    paper, we discuss obtaining an open vocabulary of expressing consent leveraging existing semantic models of provenance, processes, permission and obligations. We also present a reference architecture for the management of data processing according to consent permission. This data management model utilizes
    the open vocabulary of consent and incorporates the change of context into the
    data processing activity. By identifying and incorporating changes to the relational context between data controllers and data subjects into the data processing model, it aims to improve the integration of data management across
    different information systems specifically adhering to the GDPR and helping
    controllers to demonstrate compliance.
    Original languageEnglish
    JournalCEUR Workshop Proceedings
    Publication statusPublished - 2017


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