On privacy-aware eScience workflows
Source of Publication
© 2020, Springer-Verlag GmbH Austria, part of Springer Nature. Computing-intensive experiments in modern sciences have become increasingly data-driven illustrating perfectly the Big-Data era. These experiments are usually specified and enacted in the form of workflows that would need to manage (i.e., read, write, store, and retrieve) highly-sensitive data like persons’ medical records. We assume for this work that the operations that constitute a workflow are 1-to-1 operations, in the sense that for each input data record they produce a single data record. While there is an active research body on how to protect sensitive data by, for instance, anonymizing datasets, there is a limited number of approaches that would assist scientists with identifying the datasets, generated by the workflows, that need to be anonymized along with setting the anonymization degree that must be met. We present in this paper a solution privacy requirements of datasets used and generated by a workflow execution. We also present a technique for anonymizing workflow data given an anonymity degree.
Belhajjame, Khalid; Faci, Noura; Maamar, Zakaria; Burégio, Vanilson; Soares, Edvan; and Barhamgi, Mahmoud, "On privacy-aware eScience workflows" (2020). Scopus Indexed Articles. 200.