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Majid Rafiei [0000−0001−7161−6927] and Wil M.P. van der Aalst [0000−0002−0955−6940] Abstract. Process mining techniques such as process discovery and conformance checking provide insights into actual processes by analyzing event data that are widely available in information systems. These data are very valuable, but often contain sensitive information, and process analysts need to balance confidentiality and utility. Privacy issues in process mining are recently receiving more attention from<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18154/rwth-2020-11373">doi:10.18154/rwth-2020-11373</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ejdvcw4fcfakjih3m3ymrtte4e">fatcat:ejdvcw4fcfakjih3m3ymrtte4e</a> </span>
more »... earchers which should be complemented by a tool to integrate the solutions and make them available in the real world. In this paper, we introduce a Pythonbased infrastructure implementing state-of-the-art privacy preservation techniques in process mining. The infrastructure provides a hierarchy of usages from single techniques to the collection of techniques, integrated as web-based tools. Our infrastructure manages both standard and nonstandard event data resulting from privacy preservation techniques. It also stores explicit privacy metadata to track the modifications applied to protect sensitive data.
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