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On privacy-aware eScience workflows

Belhajjame, Khalid; Faci, Noura; Maamar, Zakaria; Burégio, Vanilson; Soares, Edvan; Barhamgi, Mahmoud (2020), On privacy-aware eScience workflows, Computing, 102, 5, p. 1171–1185. 10.1007/s00607-019-00783-8

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Belhajjame2020.pdf (438.5Kb)
Type
Article accepté pour publication ou publié
Date
2020
Journal name
Computing
Volume
102
Number
5
Publisher
Springer
Pages
1171–1185
Publication identifier
10.1007/s00607-019-00783-8
Metadata
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Author(s)
Belhajjame, Khalid
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Faci, Noura
Maamar, Zakaria
Burégio, Vanilson
Soares, Edvan
Barhamgi, Mahmoud
Abstract (EN)
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.
Subjects / Keywords
Privacy; e-Science; Workflow

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  • Thumbnail
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    A Domain-Independent Ontology for Capturing Scientific Experiments 
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    LabelFlow: Exploiting Workflow Provenance to Surface Scientific Data Provenance 
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