Control-flow Reconstruction Attacks on Business Process Models
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916397274103808 |
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| author | Kirchmann, Henrik Fahrenkrog-Petersen, Stephan A. Mannhardt, Felix Weidlich, Matthias |
| author_facet | Kirchmann, Henrik Fahrenkrog-Petersen, Stephan A. Mannhardt, Felix Weidlich, Matthias |
| contents | Process models may be automatically generated from event logs that contain as-is data of a business process. While such models generalize over the control-flow of specific, recorded process executions, they are often also annotated with behavioural statistics, such as execution frequencies.Based thereon, once a model is published, certain insights about the original process executions may be reconstructed, so that an external party may extract confidential information about the business process. This work is the first to empirically investigate such reconstruction attempts based on process models. To this end, we propose different play-out strategies that reconstruct the control-flow from process trees, potentially exploiting frequency annotations. To assess the potential success of such reconstruction attacks on process models, and hence the risks imposed by publishing them, we compare the reconstructed process executions with those of the original log for several real-world datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10986 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Control-flow Reconstruction Attacks on Business Process Models Kirchmann, Henrik Fahrenkrog-Petersen, Stephan A. Mannhardt, Felix Weidlich, Matthias Databases Artificial Intelligence Software Engineering Process models may be automatically generated from event logs that contain as-is data of a business process. While such models generalize over the control-flow of specific, recorded process executions, they are often also annotated with behavioural statistics, such as execution frequencies.Based thereon, once a model is published, certain insights about the original process executions may be reconstructed, so that an external party may extract confidential information about the business process. This work is the first to empirically investigate such reconstruction attempts based on process models. To this end, we propose different play-out strategies that reconstruct the control-flow from process trees, potentially exploiting frequency annotations. To assess the potential success of such reconstruction attacks on process models, and hence the risks imposed by publishing them, we compare the reconstructed process executions with those of the original log for several real-world datasets. |
| title | Control-flow Reconstruction Attacks on Business Process Models |
| topic | Databases Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2409.10986 |