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Privacy-enhanced BPMN: enabling data privacy analysis in business processes models

Abstract

Privacy-enhancing technologies play an important role in preventing the disclosure of private data as information is transmitted and processed. Although business process model and notation (BPMN) is well suited for expressing stakeholder collaboration and business processes support by technical solutions, little is done to depict and analyze the flow of private information and its technical safeguards as it is disclosed to process participants. This gap motivates the development of privacy-enhanced BPMN (PE-BPMN)—a BPMN language for capturing PET-related activities in order to study the flow of private information and ease the communication of privacy concerns and requirements among stakeholders. We demonstrate its feasibility in a mobile app scenario and present techniques to analyze information disclosures identified by models enriched with PE-BPMN.

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Notes

  1. 1.

    For a visualization of published leaks, see http://www.informationisbeautiful.net/visualizations/worlds-biggest-data-breaches-hacks/.

  2. 2.

    For a brief overview of BPMN and its salient features, see the OMG introduction to BPMN at https://www.omg.org/bpmn/Documents/Introduction_to_BPMN.pdf.

  3. 3.

    The PETs included in our abstract syntax are selected based on their applicability to the real-world scenario in Sect. 7, and they are sufficiently different from each other to allow discussing various details of the concrete stereotypes.

  4. 4.

    https://software.intel.com/en-us/sgx.

  5. 5.

    DARPA Brandeis—http://www.darpa.mil/program/brandeis.

  6. 6.

    https://github.com/bpmn-io/bpmn-js.

  7. 7.

    https://pleak.io/ and https://github.com/pleak-tools.

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Acknowledgements

The authors would like to thank Prof. Marlon Dumas, Peeter Laud, Dan Bogdanov and other members of the NAPLES project for discussions, comments and feedback concerning this study. This research was, in part, funded by the Air Force Research laboratory (AFRL) and Defense Advanced Research Projects Agency (DARPA) under contract FA8750-16-C-0011. The views expressed are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government. This work was also supported by the European Regional Development Fund through the Excellence in IT in Estonia (EXCITE) and by the Estonian Research Council under Institutional Research Grant IUT27-1.

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Correspondence to Jake Tom.

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Communicated by Dr Benoit Combemale.

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Pullonen, P., Tom, J., Matulevičius, R. et al. Privacy-enhanced BPMN: enabling data privacy analysis in business processes models. Softw Syst Model 18, 3235–3264 (2019). https://doi.org/10.1007/s10270-019-00718-z

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Keywords

  • Privacy
  • Business process model and notation (BPMN)
  • Privacy-enhancing technology (PET)
  • Information disclosure