On Provenance and Privacy

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.

Provided by: Association for Computing Machinery Topic: Data Management Date Added: Mar 2011 Format: PDF

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