Model-based Abstraction of Data Provenance

Christian W. Probst, René Rydhof Hansen

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    Abstract

    Identifying provenance of data provides insights to the origin of data and intermediate results, and has recently gained increased interest due to data-centric applications. In this work we extend a data-centric system view with actors handling the data and policies restricting actions. This extension is based on provenance analysis performed on system models. System models have been introduced to model and analyse spatial and organisational aspects of organisations, to identify, e.g., potential insider threats. Both the models and analyses are naturally modular; models can be combined to bigger models, and the analyses adapt accordingly. Our approach extends provenance both with the origin of data, the actors and processes involved in the handling of data, and policies applied while doing so. The model and corresponding analyses are based on a formal model of spatial and organisational aspects, and static analyses of permissible actions in the models. While currently applied to organisational models, our approach can also be extended to work flows, thus targeting a more traditional model of provenance.
    Original languageEnglish
    Title of host publication6th USENIX Workshop on the Theory and Practice of Provenance
    Place of PublicationBerkeley, California
    PublisherUSENIX Association
    Number of pages4
    Publication statusPublished - Jun 2014
    Event6th USENIX Workshop on the Theory and Practice of Provenance, TaPP 2014 - Cologne, Germany
    Duration: 12 Jun 201413 Jun 2014
    Conference number: 6
    https://www.usenix.org/conference/tapp2014

    Workshop

    Workshop6th USENIX Workshop on the Theory and Practice of Provenance, TaPP 2014
    Abbreviated titleTaPP
    CountryGermany
    CityCologne
    Period12/06/1413/06/14
    Internet address

    Keywords

    • EC Grant Agreement nr.: FP7/318003
    • EC Grant Agreement nr.: FP7/2007-2013

    Cite this

    Probst, C. W., & Hansen, R. R. (2014). Model-based Abstraction of Data Provenance. In 6th USENIX Workshop on the Theory and Practice of Provenance Berkeley, California: USENIX Association.