Abstract
Workflows are a notation for business processes, focusing on tasks and data flows between them.We have designed and implemented a method for analyzing leakages in workflows by combining differential privacy and mutual information. The input of the method is a description of leakages for each workflow component, using either differential-privacyor mutual-information-based quantification (whichever is known for the component). The differential-privacy-based bounds are combined using the triangle inequality and are then converted to mutual-informationbased bounds. Then the bounds for the components are combined using a maximum-flow algorithm. The output of the method is a mutualinformation-based quantification of leakages of the whole workflow.
Cite
CITATION STYLE
Pettai, M., & Laud, P. (2017). Combining differential privacy and mutual information for analyzing leakages in workflows. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10204 LNCS, pp. 298–319). Springer Verlag. https://doi.org/10.1007/978-3-662-54455-6_14
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