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Provenance in scientific workflows is a double-edged sword. On the one hand, recording information about the module executions used to produce a data item, as well as the parameter settings and intermediate data items passed between module executions, enables transparency and reproducibility of results. On the other hand, a scientific workflow often contains private or confidential data and uses proprietary modules. Hence, providing exact answers to provenance queries over all executions of the workflow may reveal private information.
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