Model-based Abstraction of Data Provenance

Christian W. Probst, René Rydhof Hansen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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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.
Probst, Christian W. ; Hansen, René Rydhof. / Model-based Abstraction of Data Provenance. 6th USENIX Workshop on the Theory and Practice of Provenance. Berkeley, California : USENIX Association, 2014.
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Probst, CW & Hansen, RR 2014, Model-based Abstraction of Data Provenance. in 6th USENIX Workshop on the Theory and Practice of Provenance. USENIX Association, Berkeley, California, 6th USENIX Workshop on the Theory and Practice of Provenance, TaPP 2014, Cologne, Germany, 12/06/14.

Model-based Abstraction of Data Provenance. / Probst, Christian W.; Hansen, René Rydhof.

6th USENIX Workshop on the Theory and Practice of Provenance. Berkeley, California : USENIX Association, 2014.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

TY - GEN

T1 - Model-based Abstraction of Data Provenance

AU - Probst, Christian W.

AU - Hansen, René Rydhof

PY - 2014/6

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N2 - 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.

AB - 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.

KW - EC Grant Agreement nr.: FP7/318003

KW - EC Grant Agreement nr.: FP7/2007-2013

M3 - Conference contribution

BT - 6th USENIX Workshop on the Theory and Practice of Provenance

PB - USENIX Association

CY - Berkeley, California

ER -

Probst CW, Hansen RR. Model-based Abstraction of Data Provenance. In 6th USENIX Workshop on the Theory and Practice of Provenance. Berkeley, California: USENIX Association. 2014